Autonomic and apoptotic, aeronautical and aerospace systems, and controlling scientific data generated therefrom

ABSTRACT

A self-managing system that uses autonomy and autonomicity is provided with the self-* property of autopoiesis (self-creation). In the event of an agent in the system self-destructing, autopoiesis auto-generates a replacement. A self-esteem reward scheme is also provided and can be used for autonomic agents, based on their performance and trust. Art agent with greater self-esteem may clone at a greater rate compared to the rate of an agent with lower self-esteem. A self-managing system is provided for a high volume of distributed autonomic/self-managing mobile agents, and autonomic adhesion is used to attract similar agents together or to repel dissimilar agents from an event horizon. An apoptotic system is also provided that accords an “expiry date” to data and digital objects, for example, that are available on the internet, which finds usefulness not only in general but also for controlling the loaning and use of space scientific data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 12/569,422, filed Sep. 29, 2009 now U.S. Pat. 8,275,724, andthis application is also a continuation-in-part of U.S. patentapplication Ser. No. 13/230,920, filed Sep. 13, 2011 now U.S. Pat.8,275,725, which in-turn is a divisional application of U.S. patentapplication Ser. No. 11/836,352, filed Aug. 9, 2007, now, U.S. Pat. No.8,041,655, which in-turn claims priority to U.S. Patent Application No.60/822,687, filed Aug. 17, 2006.

ORIGIN OF THE INVENTION

The invention described herein was made by an employee of the UnitedStates Government and may be manufactured and used by or for theGovernment of the United States of America for governmental purposeswithout the payment of any royalties thereon or therefor.

FIELD OF THE INVENTION

This invention relates generally to artificial intelligence and, moreparticularly, to architecture for collective interactions betweenautonomous entities.

BACKGROUND OF THE INVENTION

Future aeronautical and aerospace systems will be highly dependent onAutonomy. Autonomy is often considered as automating the tasks to beperformed by that system. Yet to achieve safe, dependable and survivableautonomous systems requires that the system is itself self-managing(selfconfiguring, self-healing, self-optimizing and self-protecting) aswell as providing the functional autonomy. One such self-managingparadigm is autonomic computing. Biological inspired autonomous andautonomic systems (AAS) are essentially about creating self-directed andself-managing systems based on metaphors such as that of the autonomicnervous system. Agent technologies have been identified as a key enablerfor engineering autonomy and autonomicity in systems, both in terms ofretrofitting into legacy systems and designing new systems. Handing overresponsibility to the systems raises concerns for humans.

Prior patents describe a technique for achieving security in agent basedsystems through the use of apoptosis, that is, the predetermined “death”of an agent unless it receives a reprieve or “stay alive” signal. Thismimics the mechanism of cell death in the human (and animal) body, andhence makes use of autonomic and other biologically inspired metaphors.The technique may also be used to send “self destruct” signals to agents(or their current hosts) that may be compromised, or which cannot beidentified as “friendly” or as having a right to access certainresources (re ALice signal disclosure).

A synthetic neural system is an information processing paradigm that isinspired by the way biological nervous systems, such as the brain,process information. Biological systems inspire system design in manyother ways as well, for example reflex reaction and health signs, natureinspired systems (NIS), hive and swarm behavior, and fire flies. Thesesynthetic systems provide an autonomic computing entity that can bearranged to manage complexity, continuous self-adjust, adjustment tounpredictable conditions, and prevention and recovery for failures.

One key element is the general architecture of the etic neural system. Asynthetic neural system is composed of a large number of highlyinterconnected processing autonomic elements that may be analogous toneurons in a brain working in parallel to solve specific problems.Unlike general purpose brains, a synthetic neural system is typicallyconfigured for a specific application and sometimes for a limitedduration.

In one application of autonomic elements, each of a number ofspacecrafts could be a worker in an autonomous space mission. The spacemission can be configured as an autonomous nanotechnology swarm (ANTS).Each spacecraft in an ANTS has a specialized mission, much like ants inan ant colony have a specialized mission. Yet, a heuristic neural system(HNS) architecture of each worker in an ANTS provides coordination andinteraction between each HNS that yields performance of the aggregate ofthe ANTS that exceeds the performance of a group of generalist workers.

More specifically, subset neural basis functions (SNBFs) within a HNScan have a hierarchical interaction among themselves much as the workersdo in the entire ANTS collective. Hence, although many activities of thespacecraft could be controlled by individual SNBFs, a ruler SNBF couldcoordinate all of the SNBFs to assure that spacecraft objectives aremet. Additionally, to have edundancy for the mission, inactive workersand rulers can only participate if a member of their type is lost.

In some situations, the ANTS encounters a challenging situation. Forexample, in some instances, the operation of a particular autonomicspacecraft can be detrimental either to the autonomic spacecraft or tothe mission. It would be desirable to have a self-destruct mechanismthat can be employed to avoid such a detrimental outcome, for example,analogous to apoptotic activity in a biological system. The need toreplace the agent or spacecraft, and how to select an agent to becomethe replacement, form bases for various embodiments of the presentteachings. Protecting scientific data obtained from such systems alsoforms the basis for various embodiments of the present teachings.

BRIEF DESCRIPTION OF THE INVENTION

The above-mentioned shortcomings, disadvantages and problems may beaddressed herein, which will be understood by reading and studying thefollowing specification.

Autonomic Computing and Autonomic Communications are inspired by thebiological Autonomic Nervous System. Apoptotic Computing and ApoptoticCommunications are inspired by the apoptosis mechanism in biologicalsystems. This mechanism provides self-management and security for theoverall system. These approaches are included in modernubiquitous/pervasive computer-based systems and next generation SWARMbased Space Missions. The present teachings adapt the autonomic andapoptotic systems described, for example, in U.S. Patent ApplicationPublication No. US 2010/0146635 A1 and in U.S. Pat. No. 8,041,655 B2 forCloud Computing, Grid Computing and other Highly Distributed Systems.Both of these publications are incorporated herein in their entiretiesby reference. These paradigms of computing axe increasingly becomeubiquitous and their management and safety is not only key for theindustry in general but also for NASA's operations and missions.

According to various embodiments of the present teachings, an autonomicdevice, system, and method are provided that include a self-managingautopoiesis function. The present teachings provide self-protection of aself-managing system and are useful in encouraging a secure feelingabout utilizing autonomy and autonomicity. These teachings build onprevious teachings described and incorporated herein, and include thenew self-* property of autopoiesis (self-creation). The Autonomic(self-managing) Environment can comprise collaborating agents to dealwith self-configuration, self-healing, self-optimizing andself-protecting aspects of the highly distributed system. These self-*agents depending on their security mechanisms may have an apoptotic(self-destruct) mechanism inbuilt relying on stay-alive or self-destructapoptotic signals to continue or desist their operations. The autonomicautopoiesis (self-creation) mechanism of the present teachings is acounter-measure to the apoptotic (self-destruct) mechanism. Sincepotentially an agent may self-destruct due to security or other factors,that necessary self-* function is no longer in existence within theself-managing system in that locale. As such, there may be a need for amechanism for auto generation of a replacement. Depending oncircumstances, this autopoietic agent may not necessarily be a clone ofthe original but an alternative, for example, providing an equivalentfunctionality. The Autopoiesis mechanism, although not directly inspiredby a biological function, is a follow on mechanism to auto replace(create) an equivalent self-* function that is missing from a system,for example, as a result of an apoptotic action. The device, system, andmethod can have many of the same or similar components, steps, and otherfeatures as described, for example, in U.S. Pat. No. 7,904,396 B2 toHinehey et al., which is incorporated herein in its entirety byreference. Patent and patent pending technologies that can be used inaccordance with the present teachings and which exemplify environmentsthat are self-managing include those describing a range of autonomiccomputing (self-managing self*) techniques. These technologies includean apoptotic (self-destruct) mechanism for SWARM agents and space craftas described, for example, in U.S. Pat. Nos. 7,627,538 B2 and 7,925,600B2, and autonomous and autonomic environments as described, for example,in U.S. Pat. No. 7,765,171 B2. Each of these three patents isincorporated herein in its entirety by reference. Other technologiesthat can also be employed to provide a self-managing computer system inaccordance with the present teachings include autonomic quiescence(self-sleep), as described in U.S. Pat. No. 7,899,760 B2, as a lessdrastic alternative to self-destruct, and ALice (Autonomic Licence)technology as described in U.S. Pat. No. 7,627,538 B2, to ensureauthenticity. Each of these two patents is also incorporated herein inits entirety by reference.

According to various embodiments of the present teachings, aself-protection mechanism referred to herein as “self-esteem” isincluded in an autonomic system to assist in encouraging a securefeeling about utilizing autonomy and autonomicity. A self-managingenvironment is provided with redundancy and dependability throughmultiple clone and non-clone versions of self-managing agents (SMA) andservices, for example, agents with similar if not identical externalfunctionality. Even cloned SMAs may have the inbuilt ability to adaptand thus evolve into something new. As such a novel Darwinian survivalof the fittest inherent property is used in the system. Essentially areward scheme (self-esteem) for the autonomic agents is enforced basedon their performance and trust. An SMA with greater self-esteem may thenbe allowed to clone at a greater rate with the opposite occurring forlower self-esteem SMAs. The system can be configured such that theapoptotic (self-destruct/self-sacrifice) mechanism kicks in for an SMAif it has reached a low enough self-esteem, for example, a level ofself-esteem that is so low that it falls below a threshold self-esteemneeded to ward off self-destruction.

The patents described and incorporated herein present autonomic/selfmanaging systems comprising an all pervasive lower/middle-ware thatperforms self-configuration, self-healing, self-optimizing, andself-protecting and thus enables the higher levelgoal—automation/autonomy—of the actual system to adaptively occur. Toensure survivability, dependability and safety, the autonomic systemscomprise static agents bound to system components with feedback controlloops, mobile agents, deliveries of self-management updates, heartbeat &pulse monitoring, and apoptotic mechanisms. The present teachings add tothese features a survival of the fittest reward scheme. The system canprovide redundancy and flexibility and be self-managing throughdifferent coding implementations of agents, for example, agents havingthe same external functionality or agents that provide the same service.A measure of performance and trust can be provided to guide the systemsself-managing behavior.

Given that the agents can, in some situations, self-adapt, and thatfuture versions will evolve to allow the system to self-generate agents,a measure of performance and trust can be especially important. As such,the self-esteem of each autonomic agent of an autonomic system can beused as a method of measuring the success of each agent at doing itstasks and adapting to new ones. As a result, successful agents can becloned, that is, they can be enabled to multiply, whereas unsuccessfulagents can be enabled to die out by triggering an apoptosis(pre-programmed death) mechanism based on a low self-esteem. Aself-managing system can be provided with the self-* property ofautopoiesis (self-creation) and the autopoiesis can be based on therelative self-esteems of plurality candidate replacement agents. In theevent of an agent in the system self-destructing, the autopoiesisproperty auto-generates a replacement, and if an existing agent isformed into the replacement the selection of the agent can be based onits self-esteem level, for example, compared to the self-esteem level ofother possible replacements.

The present teachings are not only applicable, in autonomic systems, todownstream activities such as self-managing mission data farms andmission control servers, but also to next generation mission classes,for example, SWARM based multiple craft missions. Accordingly, thepresent teachings also encompasses applications in missions such as ANTSwhich use high numbers of cooperating entities having inbuiltadaptability and requiring a self-managing operating system to ensurethe success of scientific goals. The device, system, and method can havemany of the same or similar components, steps, and other features asdescribed, for example, in U.S. Pat. No. 7,904,396 B2 to Hinchey et al.,which is incorporated herein in its entirety by reference. Patent andpatent pending technologies that can be used in accordance with thepresent teachings and which exemplify environments that areself-managing include those describing a range of autonomic computing(self-managing/self*) techniques. These technologies include anapoptotic (self-destruct) mechanism for SWARM agents and space craft asdescribed, for example, in U.S. Pat. Nos. 7,627,538 B2 and 7,925,600 B2,and autonomous and autonomic environments as described, for example, inU.S. Pat. No. 7,765,171 B2. Each of these three patents is incorporatedherein in its entirety by reference. Other technologies that can also beemployed to provide a self-managing computer system in accordance withthe present teachings include autonomic quiescence (self-sleep), asdescribed in U.S. Pat. No. 7,899,760 B2, as a less drastic alternativeto self-destruct, and ALice (Autonomic Licence) technology as describedin U.S. Pat. No. 7,627,538 B2, to ensure authenticity. Each of these twopatents is also incorporated herein in its entirety by reference.

According to yet other embodiments of the present teachings, abiologically inspired self-management mechanism is provided forautonomous and autonomic systems, which is referred to herein as“autonomic adhesion.” Following biological inspiration, cellularadhesion is the binding of a cell to a surface, extracellular matrix, oranother cell using cell adhesion molecules such as selectins, integrins,and cadherins. Correct cellular adhesion is essential in maintainingmulticellular structure. Cellular adhesion can link the cytoplasm ofcells and can be involved in signal transduction. Taking inspiration(and not mimicry) and applying this to autonomic agents or craft, thiscan essentially be considered akin to agents that like or dislike eachother. The present teachings provide a self-managing system here thereare a high volume of distributed autonomic/self-managing mobile agents,and autonomic adhesion is used to attract similar agents together or torepel dissimilar agents from an event horizon. For instance, similartypes of SMAs that can deal with various types of viruses would beattracted to each other at a local to deal with a virus outbreak, whileat the same time repelling other SMAs such as self-optimization agentsfrom the busy scene to avoid congestion. As a result, the properprocessor cycles can be dedicated to the incident. Just as the cellularadhesion has signal transduction, the autonomic adhesion adds adhesionsignaling to existing autonomic signals (I am alive, I am healthy,lubdub of I & environment are healthy, stay alive, self-destruct, stayawake, self-sleep, ALice signal and so forth).

The autonomic adhesive mechanism is a self-* function that enablesdynamic clustering of similar self-managing agents or crafts, so thatthey can together deal with a significant self-managing task orincident, while repelling differential SMAs or crafts and thusprioritizing available locally situated bandwidth and processor power toa task or incident at hand. Adhesion can mean physically moving closelytogether but can also more generally mean working together even if notphysically coming together. The device, system, and method can have manyof the same or similar components, steps, and other features asdescribed, for example, in U.S. Pat. No. 7,904,396 B2 to Hinchey et al.,which is incorporated herein in its entirety by reference. Patent andpatent pending technologies that can be used in accordance with thepresent teachings and which exemplify environments that areself-managing include those describing a range of autonomic computing(self-managing/self*) techniques. These technologies include anapoptotic (self-destruct) mechanism for SWARM agents and space craft asdescribed, for example, in U.S. Pat. Nos. 7,627,538 B2 and 7,925,600 B2,and autonomous and autonomic environments as described, for example, inU.S. Pat. No. 7,765,171 B2. Each of these three patents is incorporatedherein in its entirety by reference. Other technologies that can also beemployed to provide a self-managing computer system in accordance withthe present teachings include autonomic quiescence (self-sleep), asdescribed in U.S. Pat. No. 7,899,760 B2, as a less drastic alternativeto self-destruct, and ALice (Autonomic Licence) technology as describedin U.S. Pat. No. 7,627,538 B2, to ensure authenticity. Each of these twopatents is also incorporated herein in its entirety by reference.

According to yet other embodiments of the present teachings, apoptoticcomputing and apoptotic communications as provided in the patentdescribed and incorporated herein is included in an apoptotic systemspecifically useful for data and digital objects and can provide, inessence, an “expiry date”. These teachings are not only useful ingeneral for privacy and safety in the modern world but also useful forcontrolling the loaning and use of space scientific data to researchers.An apoptotic mechanism is hereby provided for data and digital objects,for example, data files, rental movie files, rental music files, limitedlicensing rights, time-based licensed access, distributed dynamic data,and the like. Although these teachings may seem counter to an objectiveof preserving data, an inherent death by default for such data anddigital objects can be a useful property for dealing with ethics,security, and trust issues. In some embodiments, data can effectivelyremain in existence, for example, available or out there on theinternet, only if it receives periodic stay alive credentials.Otherwise, the data could expire, be subject to password access only, orlocked-down. In some embodiments, the system and method can be utilizedfor licensing and/or loaning scientific data to users, for example,lending or loaning NASA space mission data.

Effectively, valuable datasets can be made to have an expiry date orother safeguard if users of the data do not correctly acknowledge,credit, or cite the data creators, and thus the stay alive could beremoved. This innovation not only applies to data but to all digitalobjects such as pictures, video or music files. Apoptotic Computing maybe considered a new sub field of Autonomic Computing. The presentteachings have particular applicability for on-line shopping services,on-line music services, on-line distributors, and the like. The device,system, and method can have many of the same or similar components,steps, and other features as described, for example, in U.S. Pat. No.7,904,396 B2 to Hinchey et al., which is incorporated herein in itsentirety by reference. Patent and patent pending technologies that canbe used in accordance with the present teachings and which exemplifyenvironments that are self-managing include those describing a range ofautonomic computing (self-managing/self*) techniques. These technologiesinclude an apoptotic (self-destruct) mechanism for SWARM agents andspace craft as described, for example, in U.S. Pat. Nos. 7,627,538 B2and 7,925,600 B2, and autonomous and autonomic environments asdescribed, for example, in U.S. Pat. No. 7,765,171 B2. Each of thesethree patents is incorporated herein in its entirety by reference. Othertechnologies that can also be employed to provide a self-managingcomputer system in accordance with the present teachings includeautonomic quiescence (self-sleep), as described in U.S. Pat. No.7,899,760 B2, as a less drastic alternative to self-destruct, and ALice(Autonomic Licence) technology as described in U.S. Pat. No. 7,627,538B2, to ensure authenticity. Each of these two patents is alsoincorporated herein in its entirety by reference.

According to various embodiments of the present teachings, an autonomicsystem provided that has the following objectives: self-configuration;self-healing; self-optimization; and self-protection. Byself-configuration, what is meant is that the system can be able toreadjust itself automatically, either to support a change incircumstances or to assist in meeting other system objectives. Byself-healing, what is meant is that, in a reactive mode, the system caneffectively recover when a fault occurs, identify the fault, and, whenpossible, repair it. In a proactive mode, self-healing can entail asystem configured to monitor vital signs to predict and avoid healthproblems, or to prevent vital signs from reaching undesirable levels. Byself-optimization, what is meant is the system can measure its currentperformance against a known optimum, and can carry out defined policiesfor attempting improvements. Self-optimization can also encompass asystem configured to react to a user's policy changes within the system.By self-protection, what is meant is that the system can defend itselffrom accidental or malicious external attacks, which requires anawareness of potential threats and the means to manage them.

According to various embodiments of the present teachings, theseself-managing objectives can be achieved be configuring the system tobe: self-aware, that is, aware of its internal state; self-situated,that is, aware of current external operating conditions and context;self-monitoring, that is, able to detect changing circumstances; andself-adjusting, that is, able to adapt accordingly. Thus, the autonomicsystems of the present teachings can be aware of its available resourcesand components, their ideal performance characteristics, and currentstatus. The system can also be aware of interconnection with othersystems, as well as rules and policies for adjusting as required. Thesystem can also operate in a heterogeneous environment, for example, byrelying on open standards to communicate with other systems.

Systems, clients, servers, methods, and computer-readable media ofvarying scope are described herein. In addition to the aspects andadvantages described in this summary, further aspects and advantageswill become apparent by reference to the drawings and by reading thedetailed description that follows.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram that provides at overview of an evolvablesynthetic neural system to manage collective interactions betweenautonomous entities, according to an embodiment of the invention;

FIG. 2 is a block diagram of a neural basis function of a worker,according to an embodiment;

FIG. 3 is a block diagram of a heuristic neural system, according to anembodiment;

FIG. 4 is a block diagram of an autonomous neural system, according toan embodiment;

FIG. 5 is a block diagram neural basis function of a worker, accordingto an embodiment;

FIG. 6 is a block diagram of a multiple level hierarchical evolvablesynthetic neural system, according to an embodiment;

FIG. 7 is a block diagram of a conventional computer cluster environmentin which different embodiments can be practiced;

FIG. 8 is a block diagram of a conventional hardware and operatingenvironment in which different embodiments can be practiced;

FIG. 9 is a block diagram of a conventional multiprocessor hardware andoperating environment in which different embodiments can be practiced;

FIG. 10 is a block diagram of a hardware and operating environment,which includes a quiese component, according to an embodiment;

FIG. 11 is a diagram of autonomous entities' interaction, according toan embodiment;

FIG. 12 is a block diagram of an autonomous entity management system,according to an embodiment;

FIG. 13 is a hierarchical chart of an autonomous entity managementsystem, according to an embodiment;

FIG. 14 is a block diagram of an autonomic element, according to anembodiment;

FIG. 15 is a block diagram of autonomy and autonomicity at a high systemlevel, according to an embodiment;

FIG. 16 is a Nock diagram of an architecture of an autonomic element,according to an embodiment, that includes reflection and reflex layers;

FIG. 17 is a flowchart of a method to construct an environment tosatisfy increasingly demanding external requirements, according to anembodiment;

FIG. 18 is a flowchart of a method to construct an environment tosatisfy increasingly demanding external requirements, according to anembodiment, where a ruler entity decides to withdraw or generate a stayalive signal;

FIG. 19 is a flowchart for a generating stay-alive signal when a warningcondition occurs, according to an embodiment;

FIG. 20 is a flowchart of a method to construct an environment tosatisfy increasingly demanding external requirements, according to anembodiment, where a ruler entity decides to withdraw or generate astay-awake signal;

FIG. 21 is a flowchart for generating an otoacoustic signal when awarning condition occurs, according to an embodiment;

FIG. 22 is a flowchart for interrogating an anonymous autonomic agent,according to an embodiment;

FIG. 23 is a flowchart of a method of autonomic communication by anautonomic element, according to an embodiment;

FIG. 24 is a flowchart of a method of autonomic communication by anautonomic element, according to an embodiment;

FIG. 25 is a flowchart of a method of autonomic communication by anautonomic element, according to an embodiment; and

FIG. 26 is a flowchart of a method of autonomic communication by anautonomic element, according to an embodiment.

FIG. 27 is a diagram depicting the ANTS concept mission scenarioaccording to various embodiments of the present teachings.

FIG. 28 depicts the multi-tier specification model of Autonomic SystemSpecification Language (ASSL) according to various embodiments of thepresent teachings.

FIG. 29 depicts a partial specification of a self-sacrifice policyaccording to various embodiments of the present teachings.

FIG. 30 depicts a partial specification of exemplary actions included ina self-sacrifice policy according to various embodiments of the presentteachings.

FIG. 31A is a schematic diagram showing a biological system wherein,when a cell constantly receives “stay alive” signals, it turns off itsprogrammed self-destruct sequence, similar to various embodiments of thepresent teachings.

FIG. 31B is a schematic diagram showing a comparison between biologicalapoptosis and biological necrosis due to an injury.

FIG. 32 is a schematic diagram of a high-level view of a simpleautonomic environment with three autonomic elements (AEs), exemplifyingvarious embodiments of the present teachings.

DETAILED DESCRIPTION OF THE INVENTION

In the following detailed description, reference is made to theaccompanying drawings that form a part hereof, and in which is shown byway of illustration specific embodiments that can be practiced. Theseembodiments are described in sufficient detail to enable those skilledin the art to practice the embodiments, and it is to be understood thatother embodiments can be utilized and that logical, mechanical,electrical and other changes can be performed without departing from thescope of the embodiments. The following detailed description is,therefore, not to be taken in a limiting sense.

The present teachings can employ a self-managing computer system thathas been developed based on autonomic computing. The autonomic computingsystem is analogous to the biological nervous system, whichautomatically maintains homeostasis (metabolic equilibrium) and controlsresponsiveness to external stimuli. An autonomic device, system, andmethod are provided that include a self-managing autopoiesis function.The autonomic autopoiesis (self-creation) mechanism can be acounter-measure to the apoptotic (self-destruct) mechanism. Sincepotentially an agent may self-destruct due to security or other factors,that necessary self-* function is no longer in existence within theself-managing system in that locale. As such, there may be a need for amechanism for auto generation of a replacement, or for an agent toself-create a replacement. Depending on circumstances, this autopoieticagent may not necessarily be a clone of the original but an alternative,for example, providing an equivalent functionality. The Autopoiesismechanism enables an auto replace (auto-create) an equivalent self-*function that is missing from a system, for example, as a result of anapoptotic action. In some embodiments, a computer-accessible medium isprovided in a first autonomic element. The computer-accessible mediumhas executable instructions of autonomic communication for directing aprocessor of the first autonomic element to perform the functions of:receiving a autopoiesis instruction from a second autonomic element; andinvoking a function of an autopoiesis component of the first autonomicelement. Then, if the first autonomic element does not receive ado-not-auto-generate reprieve signal after a predetermined period oftime, the first autonomic element undergoes autopoiesis. The function ofthe autopoiesis component can comprise modifying the autonomic elementto self-create a modified element. The instructions can further direct aprocessor to transmit self health/urgency data, and transmit environmenthealth/urgency data. The instructions can further direct a processor toreceive the environment health/urgency data from an environment controlloop component of the autonomic element.

In some embodiments, an autonomic element is provided that comprises: aself-monitor; a self-adjuster; an environment-monitor; an autonomicmanager communications component; and an autopoiesis component. Thenself-monitor can receive information from sensors, monitor and analyzethe sensor information, and access a knowledge repository. The aself-adjuster can be operably coupled to the self-monitor in a selfcontrol loop. The self adjuster can access the knowledge repository,transmit data to effectors, plan, and execute. The environment-monitorcan receive information from the sensors, monitor and analyze the sensorinformation, and access the knowledge repository. The autonomic managercommunications component can be operably coupled to theenvironment-monitor in an environment control loop. The autonomicmanager communications component can also access the knowledgerepository and produce and transmit a pulse monitor signal. The pulsemonitor signal can include a heart beat monitor signal and a reflexsignal. The reflex signal include self health/urgency data andenvironment health/urgency data. The autopoiesis component can beoperably coupled to the self-monitor and can receive an autopoiesisinstruction from another autonomic element. The autopoiesis componentcan withdraw a do-not-auto-generate signal, and then, if the autonomicelement does not receive a do-not-auto-generate reprieve signal after apredetermined period of time, the autonomic element can auto-generateinto a modified agent. The self health/urgency data can compriseuncompressed self health/urgency data, for example, wherein theenvironment health/urgency data further comprises uncompressedenvironment health/urgency data. The autonomic manager communicationscomponent can transmit the environment health/urgency data and the selfhealth/urgency data together. The autonomic manager communicationscomponent can encapsulate the environment health/urgency data and theself health/urgency data in a packet. The pulse monitor signal cancomprise at least one of an urgency signal, an environmental condition,and an event condition.

In some embodiments, a computer-accessible medium is provided havingexecutable instructions to construct an environment to satisfyincreasingly demanding external requirements. The executableinstructions can be capable of directing a processor to perform thefunctions of instantiating a first embryonic evolvable neural interface;and evolving the first embryonic evolvable neural interface towardscomplex connectivity, wherein the evolvable neural interface receivesone or more heart beat monitor signal, pulse monitor signal, andautopoiesis signals. The evolvable neural interface can generate one ormore heart beat monitor signal, pulse monitor signal, and autopoiesissignal, wherein the first evolvable neural interface receives anautopoiesis signal from a second evolvable neural interface. If thefirst evolvable neural interface does not receive a do-not-self-generatereprieve signal after a predetermined period of time, the firstevolvable neural interface can undergo autopoiesis. The embryonicevolvable neural interface can further comprise a neural threadpossessing only a primitive and minimal connectivity. The autopoiesissignal can further comprise a stay-alive/stay-awake signal.

In some embodiments, a method is provided for maintaining an autonomicsystem after destruction of an agent of the system. The method cancomprise: determining the potential benefit of having one or moreautonomic agent of the system undergo autopoiesis to create a modifiedagent; sending a request-for-self-esteem-level signal to the one or moreautonomic agent; and monitoring the response of the one or moreautonomic agent to the request-for-self-esteem-level signal. If theautonomic agent does not receive a do-not-self-generate reprieve signalafter a predetermined period of time, the autonomic agent can undergoautopoiesis to create the modified agent. In some cases, the one or moreautonomic agent is a messenger agent, and the modified agent is a ruleragent. In some cases, in response to the request-for-self-esteem-levelsignal, the one or more autonomic agent can send a signal indicative ofa level of self-esteem that is below a threshold level, and if so, themethod can further comprise then sending a do-not-self-generate signalto the one or more autonomic agent. In some cases, in response to therequest-for-self-esteem-level signal, the one or more autonomic agentsends a signal indicative of a level of self-esteem that is above athreshold level, and the method then further comprises: sending ado-not-self-generate reprieve signal to the one or more autonomic agent;and causing the one or more autonomic agent to undergo autopoiesis.

According to yet other embodiments of the present teachings, acomputer-accessible medium is provided having executable instructions toprotect an autonomic system when encountering one or more autonomicagent. The executable instructions can be capable of directing aprocessor of an autonomic agent to perform the functions of: sending arequest-for-self-esteem-level signal to the autonomic agent; monitoringthe response of the autonomic agent to the request-for-self-esteem-levelsignal; and determining the autonomic agent potential for autopoiesis.Then, if the autonomic agent does not receive a do-not-autopoiesisreprieve signal after a predetermined period of time, the autonomicagent can undergo autopoiesis. The computer-accessible medium canfurther comprise instructions for: controlling the autonomic systembased on the autonomic agent potential for autopoiesis, wherein arequest-for-self-esteem-level signal is a request for the autonomicagent to undergo autopoiesis. The controlling the autonomic system canfurther comprise granting the autonomic agent access to certainresources; and generating a signal to the autonomic agent to transmit anautopoiesis signal. In some cases, the controlling the autonomic systemcan further comprise generating a signal to the autonomic agent towithdraw the autopoiesis signal.

According to various embodiments of the present teachings, acomputer-accessible medium is provided having executable instructionsfor managing a self-similar neural system based on its functioningstatus and operating state. The computer-accessible medium can comprise:computer executable self-similar neural code to generate one or morestay-alive signals based on the functioning status and operating stateof the system, by a processor. The stay-alive signals can include one ormore of a withdrawing of a stay-alive signal, an initiating aself-destruct sequence signal, an initiating autopoiesis sequencesignal, or a continuing to stay alive signal. The one or more stay-alivesignals can be based on one or more received signals from the system,and the received signals can be indicative of the functioning status andoperating state, to obtain an analysis of the condition of the system.The processor can process the one or more received signals and therebymanage operations and resources of the system. The functioning status ofthe system can be one or more on signal, off signal, active signal, orinactive signal. The operating state of the system can be indicated byone or more urgency signal, reflex signal, environmental condition, orevent condition. An event condition can be one or more incorrectoperation, emergent behavior, failure to perform self healing, orlikelihood of jeopardizing primary objectives. In some cases, the one ormore stay-alive signals comprises an initiating autopoiesis sequencesignal that includes instructions for modifying an agent of the systemto undergo autopoiesis and become a modified agent.

According to various embodiments of the present teachings, aself-protection referred to herein as “self-esteem” is included in anautonomic system to assist in encouraging a secure feeling aboututilizing autonomy and autonomicity. A self-managing environment isprovided with redundancy and dependability through multiple clone andnon-clone versions of self-managing agents (SMA) and services, forexample, agents with similar if not identical external functionality.Even cloned SMAS may have the inbuilt ability to adapt and thus evolveinto something new. As such a novel Darwinian survival of the fittestinherent property is used in the system. A reward scheme, referred toherein as self-esteem, is provided for the autonomic agents and isenforced based on their performance and trust. As an SMA is moreimportant or successful in accomplishing its goals, the system canreward the SMA by sending a signal instructing the SMA to elevate itsself-esteem value. Alternatively, the SMA can be provided with goals,and once a goal is achieved, the SMA can be preprogrammed to then raiseits own level of self-esteem. If an SMA is not reaching its goals, or atime period passes without the SMA accomplishing a goal, then similarlythe self-esteem of that SMA can be lowered, either based on a signalsent from another agent or based on a preprogrammed protocol. An SMAwith greater self-esteem may then be allowed to clone at a greater ratewith the opposite occurring for lower self-esteem SMAs. The system canbe configured such that the apoptotic (self-destruct/self-sacrifice)mechanism kicks in for an SMA if it has reached a low enoughself-esteem, for example, a level of self-esteem that is so low that itfalls below a threshold self-esteem needed to ward off self-destruction.

The present disclosure provides autonomic/self managing systemscomprising an all pervasive lower/middle-ware that performsself-configuration, self-healing, self-optimizing, and self-protectingand thus enables the higher level goal of automation and autonomy toadaptively occur. To ensure survivability, dependability and safety, theautonomic systems comprise static agents bound to system components withfeedback control loops, mobile agents, deliveries of self-managementupdates, heartbeat & pulse monitoring, and apoptotic mechanisms. Thepresent teachings add to these features a survival of the fittest rewardscheme. The system can provide redundancy and flexibility and beself-managing through different coding implementations of agents, forexample, agents having the same external functionality or agents thatprovide the same service. A measure of performance and trust can beprovided to guide the systems self-managing behavior. In an exemplarysystem, a first self managing agent (SMA-1) and a second self managingagent (SMA-2) are each tasked with photographing craters on the surfaceof a planet. SMA-1 finds and photographs 200 craters on the Westernhemisphere of the planet whereas SMA-2 finds and photographs only threecraters on the Eastern hemisphere of the planet. After a predeterminedperiod of three months, SMA-1 and SMA-2 each look into their respectiveinformation repositories to see whether their work entitles them to aself-esteem reward. The information repository indicates that eachhemisphere of the planet was estimated to have about 190 craters, soSMA-1 found most, if not all, of the craters and is rewarded with anelevated self-esteem level, for example, a self-esteem value increase oftwo points. SMA-2 barely found any craters at all, and is thus penalizedby reducing its self-esteem level, for example, by decreasing itsself-esteem value by two points. SMA-2 could be malfunctioning, or theestimate might be wrong such that there is not anywhere close to 190craters in the Eastern hemisphere. Either way, the value of SMA-2 to thescientific mission of mapping and photographing craters on the planet isfar less than the value of SMA-1 to the mission. As a result, theoverall system including SMA-1 and SMA-2 has more trust in SMA-1, andthat trust is measured by the self-esteem level. Therefore, if there isa need to send one SMA to a different planet to map and photographcraters, SMA-1 would be considered a better candidate for that missiondue to its higher level of self-esteem. Furthermore, if the system is tobe simplified be deactivating or self-destructing one or more SMA, SAM-2would be a better candidate for deactivation or self-destruction as itslevel of self-esteem indicates it is less important and/or valuable tothe overall system compared to SMA-1. Also, if a need exists for an SMAto self-create a new agent by autopoiesis, SMA-1 would be a bettercandidate for autopoiesis compared to SMA-2 because of the higher levelof self-esteem that had been rewarded to SMA-1. The new agent might bean asteroid belt, photographing agent having different goals andprogramming than had been provided for either SMA-1 or SMA-2.Regardless, however, of the goals of the new agent, the respectiveself-esteem levels provide the overall system with greater confidencethat SMA-1 can perform the new task as opposed to SMA-2.

According to various embodiments, a method for maintaining an autonomicsystem comprising a plurality of self-managing agents, is provided. Themethod can comprise: measuring the performance, trust, or both, of eachself-managing agent of the autonomic system; sending arequest-for-self-esteem-level signal to each of the one or moreself-managing agents; monitoring the response of each self-managingagent to the respective request-for-self-esteem-level signal; andmodifying the autonomic system based on the responses. The measuring cancomprise measuring the performance of each self-managing agent of theautonomic system. The one or more of the responses can indicate that oneor more of the self-managing agents are faulty, below average, notoptimal, or outside an acceptable range of values, based on a respectivelevel of self-esteem that is below a threshold value. The one or more ofthe responses can indicate that one or more of the self-managing agentsare fit or not fit for a particular task, mission, ability, or commandlevel, based on a respective level of self-esteem. Modifying theautonomic system can comprise causing one or more of the self-managingagents to self-destruct, for example, if it is determined from theresponse that the self-managing agent is not fit for a particular taskor is faulty. The one or more responses can indicate that one or more ofthe self-managing agents has a higher level of self-esteem than one ormore of the other self-managing agents, and in such a case, themodifying the autonomic system can comprise causing the one or moreself-managing agents with the higher level of self-esteem to receive aself-destruct reprieve signal. In some cases, the one or more responsescan indicate that one or more of the self-managing agents has a higherlevel of self-esteem than one or more of the other self-managing agents,and the modifying the autonomic system can comprise causing the one ormore self-managing agents with the higher level of self-esteem toundergo autopoiesis. According to some embodiments, an autonomic systemis provided that comprises a plurality of self-managing agents and isconfigured to carry out such a method based on levels of self-esteem,for example, based on relative levels of self-esteem.

In some embodiments, a computer-accessible medium is provided havingexecutable instructions to protect an autonomic system when encounteringone or more self-managing agents of the autonomic system. The executableinstructions can be configured to direct a processor of an autonomicagent to perform the functions of: measuring the performance, trust, orboth, of each of the self-managing agents encountered; sending arequest-for-self-esteem-level signal to each of the self-managing agentsencountered; monitoring the response of each self-managing agent to therespective request-for-self-esteem-level signal; and modifying theautonomic system based on the response from each self-managing agentencountered. The executable instructions can be configured to direct aprocessor of an autonomic agent to measure the performance of eachself-managing agent of the autonomic system. The executable instructionscan be configured to direct a processor of an autonomic agent to modifythe autonomic system to cause a faulty, below average, not optimal,unacceptable, below threshold, or unfit self-managing agent toself-destruct based on a respective level of self-esteem. For example,the self-managing agent can self-destruct if it is not fit for aparticular task, mission, ability, or command level. The executableinstructions can be configured to direct a processor of an autonomicagent to modify the autonomic system to cause one or more self-managingagents having a relatively higher level of self-esteem to receive aself-destruct reprieve signal. The executable instructions can beconfigured to direct a processor of an autonomic agent to modify theautonomic system to cause one or more self-managing agents having arelatively higher level of self-esteem to undergo autopoiesis.

Given that the agents can, in some situations, self-adapt, and thatfuture versions will evolve to allow the system to self-generate agents,a measure of performance and trust can be especially important. As such,the self-esteem of each autonomic agent of an autonomic system can beused as a method of measuring the success of each agent at doing itstasks and adapting to new ones. As a result, successful agents can becloned enabled to multiply whereas unsuccessful agents can be enabled todie out by triggering an apoptosis (pre-programmed death) mechanismbased on a low self-esteem. A self-managing system can be provided withthe self-* property of autopoiesis (self-creation) and the autopoiesiscan be based on the relative self-esteems of plurality candidatereplacement agents. In the event of an agent in the systemself-destructing, the autopoiesis property auto-generates a replacement,and if an existing agent is formed into the replacement the selection ofthe agent can be based on its self-esteem level, for example, comparedto the self-esteem level of other possible replacements.

The present teachings are not only applicable, in autonomic systems, todownstream activities such as self-managing mission data farms andmission control servers, but also to next generation mission classes,for example, SWARM based multiple craft missions. Accordingly, thepresent teachings also encompasses applications in missions such as ANTSwhich use high numbers of cooperating entities having inbuiltadaptability and requiring a self-managing operating system to ensurethe success of scientific goals.

According to yet other embodiments of the present teachings, abiologically inspired self-management mechanism is provided forautonomous and autonomic systems, which is referred to herein as“autonomic adhesion.” Following biological inspiration, cellularadhesion is the binding of a cell to a surface, extracellular matrix, oranother cell using cell adhesion molecules such as selectins, integrins,and cadherins. Correct cellular adhesion is essential in maintainingmulticellular structure. Cellular adhesion can link the cytoplasm ofcells and can be involved in signal transduction. Taking inspiration(and not mimicry) and applying this to autonomic agents or craft, thiscan essentially be considered akin to agents that like or dislike eachother. The present teachings provide a self-managing system where thereare a high volume of distributed autonomic/self-managing mobile agents,and autonomic adhesion is used to attract similar agents together or torepel dissimilar agents from an event horizon. For instance, similartypes of SMAs that can deal with various types of viruses would beattracted to each other at a local to deal with a virus outbreak, whileat the same time repelling other SMAs such as self-optimization agentsfrom the busy scene to avoid congestion. As a result, the properprocessor cycles can be dedicated to the incident.

In an exemplary embodiment, an autonomic system can provide a highresolution composite photograph of a nebula by using multiple camerasfrom multiple spacecraft of a swarm. Different agents or spacecrafts ofthe swarm can receive signals requesting all spacecraft with infraredcameras to adhere together so the composite photograph can be taken. Atthe same time, those spacecraft of the swarm that do not have infraredcameras are not needed and would be in the way of the others that are tobe used in generating the composite photograph. As a result, thosespacecraft of the swarm that do not have infrared cameras may receivethe same “adhered at location ‘X’ if you have an infrared camera” butthey would interpret the signal as “repel from the group” because theydo not have an infrared camera. En some cases, rather than each agentreceiving the same signal, different signals can be sent to and/orreceived by each agent of the swarm, for example, depending on thehardware, firmware, software, or a combination thereof, known to existin each particular agent. So agents with infrared cameras might receivean “adhere at rally point A” signal whereas agents without an infraredcamera might receive a “stay away from rally point A” signal.

Just as the cellular adhesion has signal transduction, the autonomicadhesion adds adhesion signaling to existing autonomic signals (I amalive, I am healthy, lubdub of I & environment are healthy, stay alive,self-destruct, stay awake, self-sleep. ALice signal and so forth).

The autonomic adhesive mechanism is a self-* function that enablesdynamic clustering of similar self-managing agents or crafts, so thatthey can together deal with a significant self-managing task orincident, while repelling differential SMAs or crafts and thusprioritizing available locally situated bandwidth and processor power toa task or incident at hand.

According to various embodiments, a method is provided for maintainingan autonomic system comprising a plurality of self-managing agents. Themethod can comprise: sending an adhere or repel signal to each of theplurality of self-managing agents; causing one or more of the pluralityof self-managing agents to rendezvous at the locale if the self-managingagent meets the requirements for adhesion; and causing one or more ofthe plurality of self-managing agents to repel away from the locale ifthe self-managing agent does not meet the requirements for adhesion. Theadhere or repel signal can comprise information pertaining torequirements for adhesion and a locale for rendezvous purposes. Themethod can further comprise causing two or more of the self-managingagents that rendezvous at the locale to work together to accomplish anobjective. The requirements can comprise one or more hardwarerequirements, firmware requirements, software requirements, acombination thereof, a scientific instrument requirement, or the like.The method can further comprise causing two or more of the self-managingagents that rendezvous at the locale to share at least one resource witheach other, for example, a power resource, a computer processingresource, a scientific instrument, battery power, fuel, a solar panel,or the like. In some cases, an autonomic system is provided thatcomprises a plurality of self-managing agents configured to carry outsuch a method.

While examples of “adhesion” are described herein that relate todifferent components of a system gathering physically close together, itis to be understood that “adhesion” can also more generally mean thatcomponents of a system would be drawn together to work together, even ifthey are not gathered closely together. In the case of ANTS and similarmissions and missions using low power, short distance transmissions, andthe like limits, the components can benefit from a rendez-vous closetogether. “Adhesion,” as used herein, encompasses components that cometogether, are inclined to work together, or both, even if not in thesame physical location, for example, to form a daisy chain of receivingand transmitting points through space. Self-adhesion means thatdifferent components may or may not rendezvous physically closelytogether but are predisposed to working together even if not in the samephysical location. Similarly, “repel” does not necessarily mean tophysically move away from one another, but instead, can more generallymean to not cooperate with one another.

According to yet other embodiments, a computer-accessible medium isprovided having executable instructions to protect an autonomic systemwhen encountering one or more self-managing agents of the autonomicsystem. The executable instructions can be configured to direct aprocessor of an autonomic agent to perform the functions of: sending anadhere or repel signal to each of the plurality of self-managing agents;causing one or more of the plurality of self-managing agents torendezvous at the locale if the self-managing agent meets therequirements for adhesion; and causing one or more of the plurality ofself-managing agents to repel away from the locale if the self-managingagent does not meet the requirements for adhesion. The adhere or repelsignal can comprise information pertaining to requirements for adhesionand a locale for rendezvous purposes. The requirements can comprise oneor more hardware requirements, firmware requirements, softwarerequirements, a combination thereof, a scientific instrumentrequirement, or the like. The executable instructions can be configuredto direct a processor of an autonomic agent to cause two or more of theself-managing agents that rendezvous at the locale to share at least oneresource with each other, for example, a power resource, a computerprocessing resource, a scientific instrument, battery power, fuel, asolar panel, or the like.

According to yet other embodiments of the present teachings, apoptoticcomputing and apoptotic communications as provided in the patentdescribed and incorporated herein is included in an apoptotic systemspecifically useful for data and digital objects and can provide, inessence, an “expiry date”. These teachings are not only useful ingeneral for privacy and safety in the modem world but also useful forcontrolling the loaning and use of space scientific data to researchers.An apoptotic mechanism is hereby provided for data and digital objects,for example, data files, rental movie files, rental music files, limitedlicensing rights, time-based licensed access, distributed dynamic data,and the like. Although these teachings may seem counter to an objectiveof preserving data, an inherent death by default for such data anddigital objects can be a useful property for dealing with ethics,security, and trust issues. In some embodiments, data can effectivelyremain in existence, for example, available or out there on theinternet, only if it receives periodic stay alive credentials.Otherwise, the data could expire, be subject to password access only, orlocked-down. In some embodiments, the system and method can be utilizedfor licensing and/or loaning scientific data to users, for example,lending or loaning NASA space mission data.

Effectively, valuable datasets can be made to have an expiry date orother safeguard if users of the data do not correctly acknowledge,credit, or cite the data creators, and thus the stay alive could beremoved. This innovation not only applies to data but to all digitalobjects such as pictures, video or music files. Apoptotic Computing maybe considered a new sub field of Autonomic Computing. The presentteachings have particular applicability for on-line shopping services,on-line music services, on-line distributors, and the like.

According to various embodiments, a computer-accessible medium isprovided having executable instructions for managing digital data or adigital object, for example, based on functioning status and operatingstate. The computer-accessible medium can comprise: computer executableself-similar neural code to generate one or more stay-alive signalsbased on the functioning status and operating state, by a processor. Thestay-alive signals can include one or more of a withdrawing-of-accesssignal, an initiating-of-self-destruct signal, or acontinuing-to-provide-access signal. The at least one stay-alive signalcan be based on one or more received signals from a user. The receivedsignals can be indicative of the accessibility to the digital data ordigital object, which is granted to the particular user. The processorcan process the one or more received signals and thereby managingoperations and accessibility of the digital data or digital object. Insome cases, the received signals indicate the user has complete,unrestricted access to the entirety of the digital data or digitalobject, or limited access to the digital data or digital object, or noaccess to the digital data or digital object, or access to a only aselected portion of the digital data or digital object. The receivedsignals can indicate that the user has access only to an abstract of thedigital data or digital object, or access for only a limited time, forexample, 24 hours. The digital data or digital object can comprisephotographs, scientific data from a space mission, a video file, a musicfile, an audio file, or the like. The one or more received signals froma user can comprise an IP address, a password, a user name, or acombination thereof. In some embodiments, a computer processing systemis provided that comprises a memory having stored therein thecomputer-accessible medium described above.

According to yet other embodiments, a method is provided for managingaccess to digital data or to a digital object. The method can comprise:receiving signals from a user; processing the one or more receivedsignals and managing operations and accessibility of the digital data ordigital object based on functioning status and operating state. Thereceived signals can be indicative of the accessibility to the digitaldata or digital object, which is granted to the user. The processing cancomprise running computer executable self-similar neural code togenerate one or more stay-alive signals based on the functioning statusand operating state. The stay-alive signals can include one or more of awithdrawing-of-access signal, an initiating-of-self-destruct signal, ora continuing-to-provide-access signal. The at least one stay-alivesignal can be based on one or more received signals from a user, and thereceived signals can be indicative of the accessibility to the digitaldata or digital object, which is granted to the user.

In various embodiments of the present teachings, a method for managing asystem includes receiving a potentially harmful signal and transmittingan otoacoustic signal to counteract the potentially harmful signal. Inother embodiments, an autonomic element includes a self-monitor that isoperable to receive information from sensors and is operable to monitorand analyze the sensor information and access a knowledge repository, aself-adjuster operably coupled to the self-monitor in a self-controlloop, the self-adjuster operable to access the knowledge repository, theself-adjuster operable to transmit data to effectors, and theself-adjuster operable to plan and execute, an environment monitor thatis operable to receive information from sensors and operable to monitorand analyze the sensor information and access the knowledge repository,and an autonomic manager communications component operably coupled tothe environment monitor in an environment control loop, the autonomicmanager communications component operable to access the knowledgerepository, the autonomic manager communications component also operableto produce and transmit a counteracting signal to an incoming harmfulsignal.

In yet other embodiments, an autonomic system includes a plurality ofautonomic agents performing one or more programmed tasks. The autonomicsystem also includes a coordinating autonomic agent for assigningprogrammed task and for issuing instructions to the plurality ofautonomic agents. The autonomic system also includes a messengerautonomic agent for facilitating communication between the coordinatingautonomic agent, plurality of autonomic agents, a remote system. One ormore programmed task performed by the plurality of autonomic objects isat least generating signals indicative of a potentially harmful signal.The coordinating autonomic agent transmits an otoacoustic signal to oneor more of the plurality of autonomic agents, based on the generatedsignals.

In still yet other embodiments, an autonomous nanotechnology swarmincludes a first worker composed of self-similar autonomic components.The autonomous nanotechnology swarm also includes a second workercomposed of self-similar autonomic components. The autonomousnanotechnology swarm also includes a third worker composed ofself-similar autonomic components. In the autonomous nanotechnologyswarm, the third worker facilitates communication between the firstworker and the second worker. In the autonomous nanotechnology swarm,the first worker generates a heart beat monitor signal and pulse monitorsignal. In the autonomous nanotechnology swarm, the second workerincludes an otoacoustic component that is operable to counteract aharmful signal.

In further embodiments, a method includes instantiating an embryonicevolvable neural interface. The method also includes evolving theembryonic evolvable neural interface towards complex completeconnectivity. The evolvable neural interface receives one or more heartbeat monitor signal, pulse monitor signal, and otoacoustic signal. Theevolvable neural interface generates one or more heart beat monitorsignal, pulse monitor signal, and otoacoustic signals.

In yet a further embodiment, a method for protecting an autonomic systemwhen encountering one or more autonomic agents includes determining thepotential harm of the autonomic agent. The method also includes sendingan otoacoustic signal to the autonomic agent and monitoring the responseof the autonomic agent to the otoacoustic signal.

In still yet a further embodiment, a system includes a processor and astorage device coupled to the processor. The system also includessoftware means operative on the processor for sending an otoacousticsignal to the autonomic agent, monitoring the response of the autonomicagent to the otoacoustic signal, and determining the autonomic agentpotential for causing harm to the autonomic system.

System Level Overview

FIG. 1 is a block diagram that provides an overview of an evolvablesynthetic neural system to manage collective interactions betweenautonomous entities, according to an embodiment. System 100 can includea first plurality of neural basis functions (NBFs) 102 and 104. NBFs arethe fundamental building block of system 100. In some embodiments ofsystem 100, the plurality of NBFs includes more than the two NBFs 102and 104 shown in FIG. 1. In some embodiments, system 100 includes onlyone NBF. One embodiment of a NBF is described below with reference toFIG. 2.

System 100 can also include a firstinter-evolvable neural interface(ENI) 106 that is operably coupled to each of the first plurality ofneural basis functions. The NBFs 102 and 104 can be highly integrated,and coupling between the NBFs through the ENI 106 provides a threedimensional complexity. Thus, for example, then system 100 isimplemented on microprocessors such as microprocessor 804 describedbelow with reference to FIG. 8, system 100 can provide a syntheticneural system that reconciles the two dimensional nature ofmicroprocessor technologies to the three dimensional nature ofbiological neural systems.

This embodiment of the inter-ENI 106 can be known as an inter-NBF ENIbecause the inter-ENI 106 is illustrated as being between or among theNBFs 102 and 104 at the same level within a hierarchy. System 100 showsonly one level 108 of a hierarchy, although one skilled in the art willrecognize that multiple hierarchies can be used within the scope of thisinvention.

System 100 can also operate autonomously. A system operates autonomouslywhen the system exhibits the properties of being self managing and selfgoverning, often termed as autonomic, pervasive, sustainable,ubiquitous, biologically inspired, organic or with similar such terms.ENI 106 can adapt system 100 by instantiating new NBFs and ENIs andestablishing operable communication paths 110 to the new NBFs and theENIs to system 100. ENI 106 can also adapt system 100 by removing ordisabling the operable communication paths 110 to the new NBFs and ENIs.The adapting, establishing, removing and disabling of the communicationpaths 110 can be performed autonomously. Thus, system 100 can satisfythe need for a synthetic neural system that performs significant taskswith complete autonomy.

System 100 can be capable of establishing and removing links to othersimilarly configured systems (not shown). Thus, the system 100 can bedescribed as self-similar.

The system level overview of the operation of an embodiment is describedin this section of the detailed description. Some embodiments canoperate in a multi-processing, multi-threaded operating environment on acomputer, such as computer 802 in FIG. 8.

While the system 100 is not limited to any particular NBF or ENI, forsake of clarity simplified NBFs and a simplified ENI are described.

Apparatus Embodiments

In the previous section, a system level overview of the operation of anembodiment is described. In this section, particular apparatus of suchan embodiment are described by reference to a series of block diagrams.Describing the apparatus by reference to block diagrams enables oneskilled in the art to develop programs, firmware, or hardware, includingsuch instructions to implement the apparatus on suitable computers, andexecuting the instructions from computer-readable media.

In some embodiments, apparatus 200-600 are implemented by a programexecuting on, or performed by firmware or hardware that is a part of acomputer, such as computer 802 in FIG. 8.

FIG. 2 is a block diagram of a neural basis function (NBF) 200 of aworker according to an embodiment. NBF 200 is illustrated as a hi-levelneural system because both high-level functions and low-level functionsare performed by NBF 200.

NBF 200 can include an intra-evolvable neural interface (intra-ENI) 202.The ENI 202 can be operably coupled to a heuristic neural system (HNS)204 and operably coupled to an autonomous neural system (ANS) 206. TheHNS 204 can perform high-level functions and the ANS 206 performslow-level functions that are often described as “motor functions” suchas “motor” 1510 in FIG. 15 below. In NBF 200, the HNS 204 and the ANS206 in aggregate can provide a function of a biological neural system.The intra-ENI 202 shown in FIG. 2 is an ENI that is wholly containedwithin an NBF, and is therefore prefixed with “intra.”

The intra-ENI 202 can send action messages to and receive requestmessages from the HNS 204 and the ANS 206 during learning and taskexecution cycles, as well as during interfacing operations between theintra-ENI and the FINS 204 and the ANS 206 when the HNS 204 and the ANS206 need to be modified as a result of other system failures ormodification of objectives. NBF 200 is illustrated as a worker NBFbecause this NBF performs functions, but does not provide instructionscommands to other NBFs.

FIG. 3 is a block diagram of a heuristic neural system 300 according toan embodiment.

The heuristic neural system GINS) 300 can be composed of a neural net302 for pattern recognition and a fuzzy logic package 304 to perform indecisions based on recognitions. Taken together the neural net 302 andthe fuzzy logic package 304 can form basis for a higher level heuristicintelligence.

FIG. 4 is a Mock diagram of an autonomous neural system 400 according toan embodiment.

The autonomous neural system (ANS) 400 can include a non-linear dynamicssimulation 402 that represents smart servo system behavior.

FIG. 5 is a block diagram of a neural basis function (NBF) 500 of aworker according to an embodiment. NBF 500 is shown as a bi-level neuralsystem.

In some embodiments, NBF 500 can include a self assessment loop (SAL)502 at each interface between autonomic components. Each SAL 502 cancontinuously gauge efficiency of operations of the combined HNS 204 andANS 206. The standards and criteria of the efficiency can be set ordefined by objectives of the NBF 500.

In some embodiments, NBF 500 can also include genetic algorithms (GA)504 at each interface between autonomic components. The GAs 504 canmodify the intra-ENI 202 to satisfy requirements of the SALs 502 duringlearning, task execution or impairment of other subsystems.

Similarly, the HNS 204 can have a SAL 502 interface and a GA 504interface to a core heuristic genetic code (CHGC) 506, and the ANS 206can have a SAL 502 interface and a GA 504 interface to a core autonomicgenetic code (CAGC) 508. The CHGC 506 and CAGC 508 can allowmodifications to a worker functionality in response to new objectives orinjury. The CHGC 506 and the CAGC 508 autonomic elements cannot be partof an operational neural system, but rather can store architecturalconstraints on the operating neural system for both parts of thebi-level system. The CHGC 506 and the CAGC 508 can both be modifiabledepending on variations in sensory inputs via GAs 504.

In some embodiments, the CHGC 506 and the CAGC 508 in conjunction withSALs 502 and GAs 504 can be generalized within this self similar neuralsystem to reconfigure the relationship between NBFs as well as to permitthe instantiation of new NBFs to increase the overall fitness of theneural system. Thus, NBF 500 can provide a form of evolution possibleonly over generations of BNF workers.

In some embodiments, NBF 500 can also include genetic algorithms 510 and512 that provide process information to the CHGC 506 and the CAGC 508,respectively. HNS 204 and ANS 206 can receive sensory input 514 and 516,respectively, process the sensory input and generate high level actions518 and low level actions 520, respectively.

FIG. 6 is a block diagram of a multiple level hierarchical evolvablesynthetic neural system (ESNS) 600 according to an embodiment.

The multiple level hierarchical ESNS 600 can include a first level ofhierarchy 602 that includes a NBF 604 and inter-ENI 606 and a ruler NBF608. A ruler NBF, such as ruler NBF 608 can perform functions and alsoprovide instructions commands to other subordinate NBFs.

The ruler NBF 608 of the first hierarchical level 602 is illustrated asbeing operably coupled to a ruler NBF 610 in a second hierarchical level612. Ruler NBF 610 can perform functions, receive instructions andcommands from other ruler NBFs that are higher in the hierarchy of theESNS 600 and also provide instructions commands to other subordinateNBFs.

The second hierarchical level 612 can also include an inter-ENI 614. Thesecond hierarchical level 612 of FIG. 6 shows the embodiment of an ESNS600 having one NBF operably coupled to an ENI. The ruler NBF 610 of thesecond hierarchical level 612 can be operably coupled to a ruler NBF 616in a third hierarchical level 618.

The third hierarchical level 616 can also include an inter-ENI 620. Thethird hierarchical level 616 of FIG. 6 shows the embodiment of an ESNS600 having more than two NBFs (e.g. 616, 622 and 624) operably coupledto an ENI.

In some embodiments, the NBFs 604, 608, 610, 616, 622 and 624 caninclude the aspects of NBFs 102 and 104 in FIG. 1 above, and/or NBF 200in FIG. 2 above. One skilled in the art will appreciate that manycombinations exist that fall within the purview of this invention.

Hardware and Operating Environments

FIGS. 7, 8, 9 and 10 are diagrams of hardware and operating environmentsin which different embodiments can be practiced. The description ofFIGS. 7, 8, 9 and 10 provide an overview of computer hardware andsuitable autonomic computing environments in conjunction with which someembodiments can be implemented. Embodiments are described in terms of acomputer executing computer-executable instructions. However, someembodiments can be implemented entirely in computer hardware in whichthe computer-executable instructions are implemented in read-onlymemory. Some embodiments can also be implemented in client/serverautonomic computing environments where remote devices that perform tasksare linked through a communications network. Program modules can belocated in both local and remote memory storage devices in a distributedautonomic computing environment. Those skilled in the art will know thatthese are only a few of the possible computing environments in which theinvention can be practiced and therefore these examples are given by wayof illustration rather than limitation.

FIG. 7 is a block diagram of a computer cluster environment 700 in whichdifferent embodiments can be practiced. System 100, apparatus 200, 300,400, 500, 600, method 2000 and ESNS 1100 and 1200 can be implemented oncomputer cluster environment 700.

Computer cluster environment 700 can include a network 702, such as anEtherFast 10/100 backbone, that is operably coupled to a cluster server704 and a plurality of computers 706, 708, 710 and 712. One possibleembodiment of the computers is computer 802 described below withreference to FIG. 8. The plurality of computers can include any numberof computers, but some implementations can include 7, 16, 32 and as manyas 512 computers. The ESNSs and NBFs described above can be distributedon the plurality of computers.

One example of the computer cluster environment 700 is a Beowolfcomputer cluster. The computer cluster environment 700 provides anenvironment in which a plurality of ESNSs and NBFs can be hosted in anenvironment that facilitates cooperation and communication between theESNSs and the NBF's.

FIG. 8 is a block diagram of a hardware and operating environment 800 inwhich different embodiments can be practiced. Computer 802 can include aprocessor 804, which can be a microprocessor, commercially availablefrom Intel, Motorola, Cyrix and others. Computer 802 can also includerandom-access memory (RAM) 806, read-only memory (ROM) 808, and one ormore mass storage devices 810, and a system bus 812, that operativelycouples various system components to the processing unit 804. The memory806, 808, and mass storage devices, 810, are illustrated as types ofcomputer-accessible media. Mass storage devices 810 can be morespecifically types of nonvolatile computer-accessible media and caninclude one or more hard disk drives, floppy disk drives, optical diskdrives, and tape cartridge drives. The processor 804 can executecomputer programs stored on the computer-accessible media.

Computer 802 can be communicatively connected to the Internet 814 via acommunication device 816. Internet 814 connectivity is well known withinthe art. In one embodiment, a communication device 816 can be a modemthat responds to communication drivers to connect to the Internet viawhat is known in the art as a “dial-up connection.” In anotherembodiment, a communication device 816 can be an Ethernet® or similarhardware network card connected to a local-area network (LAN) thatitself is connected to the Internet via what is known in the art as a“direct connection” (e.g., T1 line, etc.).

A user can enter commands and information into the computer 802 throughinput devices such as a keyboard 818 or a pointing device 820. Thekeyboard 818 can permit entry of textual info into computer 802, asknown within the art, and embodiments are not limited to any particulartype of keyboard. Pointing device 820 can permit the control of thescreen pointer provided by a graphical user interface (GUI) of operatingsystems such as versions of Microsoft Windows®, Embodiments are notlimited to any particular pointing device 820. Such pointing devices caninclude mice, touch pads, trackballs, remote controls and point sticks.Other input devices (not shown) could include a microphone, joystick,game pad, satellite dish, scanner, or the like.

In some embodiments, computer 802 can be operatively coupled to adisplay device 822. Display device 822 can be connected to the systembus 812. Display device 822 permits the display of information,including computer, video and other information, for viewing by a userof the computer. Embodiments are not limited to any particular displaydevice 822. Such display devices can include cathode ray tube (CRT)displays (monitors), as well as flat panel displays such as liquidcrystal displays (LCDs). In addition to a monitor, computers cantypically include other peripheral input/output devices such as printers(not shown). Speakers 824 and 826 provide audio output of signals.Speakers 824 and 826 can also be connected to the system bus 812.

Computer 802 can also include an operating system (not shown) that couldbe stored on the computer accessible media RAM 806, ROM 808, and massstorage device 810, and can be and executed by the processor 804.Examples of operating systems include Microsoft Windows®, Apple MacOS®,Linux®, UNIX®. Examples are not limited to any particular operatingsystem, however, and the construction and use of such operating systemsare well known within the art.

Embodiments of computer 802 are not limited to any type of computer 802.In varying embodiments, computer 802 can comprise a PC-compatiblecomputer, a MacOS®—compatible computer, a Linux®-compatible computer, ora UNIX®-compatible computer. The construction and operation of suchcomputers are well known within the art.

Computer 802 can be operated using at least one operating system toprovide a graphical user interface (GUI) including a user-controllablepointer. Computer 802 can have at least one web browser applicationprogram executing within at least one operating system, to permit usersof computer 802 to access an intranet, extranet or Internetworld-wide-web pages as addressed by Universal Resource Locator (URL)addresses. Examples of browser application programs include NetscapeNavigator® and Microsoft Internet Explorer®.

The computer 802 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer828. These logical connections can be achieved by a communication devicecoupled to, or a part of, the computer 802. Embodiments are not limitedto a particular type of communications device. The remote computer 828could be another computer, a server, a router, a network PC, a client, apeer device or other common network node. The logical connectionsdepicted in FIG. 8 include a local-area network (LAN) 830 and awide-area network (WAN) 832. Such networking environments arecommonplace in offices, enterprise-wide computer networks, intranets,extranets and the Internet.

When used in a LAN-networking environment, the computer 802 and remotecomputer 828 can be connected to the local network 830 through networkinterfaces or adapters 834, which is one type of communications device816. Remote computer 828 can also include a network device 836. Whenused in a conventional WAN-networking environment, the computer 802 andremote computer 828 can communicate with a WAN 832 through modems (notshown). The modem, which can be internal or external, is connected tothe system bus 812. In a networked environment, program modules depictedrelative to the computer 802, or portions thereof, can be stored in theremote computer 828.

Computer 802 can also include power supply 838. Each power supply can bea battery.

FIG. 9 is a block diagram of a multiprocessor hardware and operatingenvironment 900 in which different embodiments can be practiced.Computer 902 can include a plurality of microprocessors, such asmicroprocessor 804, 904, 906, and 908. The four microprocessors ofcomputer 902 can be one example of a multi-processor hardware andoperating environment; other numbers of microprocessors can be used inother embodiments.

Similar to the computer cluster environment 700 in FIG. 7 above, thecomputer 902 can provide an environment in which a plurality of ESNSsand NBFs can be hosted in an environment that facilitates cooperationand communication between the ESNSs and the NBFs.

FIG. 10 is a block diagram of a hardware and operating environment 1000which can include a quiese component, according to an embodiment. Thehardware and operating environment 1000 reduces the possibility that anautonomic element will jeopardize the mission of the autonomic unit.

A quiesce component 1002 of an autonomic unit can render the autonomicunit inactive for a specific amount of time or until a challengingsituation has passed. The quiesce component 1002 can be invoked wheneither an external supervisory entity or the autonomic unit itselfdetermines that the autonomic unit could best serve the needs of theswarm by quiescing. Quiescing can render the autonomic unit temporarilyinactive or disabled. Thus, the quiesce component 1002 can reduce thepossibility that an autonomic element will jeopardize the mission of theautonomic element by deactivation or inactivating the autonomic element.

Quiesce time can be defined as the length of time taken to quiesce asystem (to render the system inactive), or the length of time betweenperiods of activity (i.e., the length of time of inactivity). Thequiescing can be somewhat analogous to the cell lifecycle, were cellscan stop dividing and go into a quiescent state.

Components of the system 100, apparatus 200, 300, 400, 500, 600, 1000,1400, 1200, 1300, 1400, 1500 and 1600 and methods 1700, 1800, 1900,2000, 2100, 2200, 2300, 2400, 2500 and 2600 can be embodied as computerhardware circuitry or as a computer-readable program, or a combinationof both.

More specifically, in one computer-readable program embodiment, theprograms can be structured in an object-orientation using anobject-oriented language such as Java, Smalltalk or C++, and theprograms can be structured in a procedural-orientation using aprocedural language such as COBOL or C. The software components cancommunicate in any of a number of ways that are well-known to thoseskilled in the art, such as application program interfaces (API) orinterprocess communication techniques such as remote procedure call(RPC), common object request broker architecture (CORBA), ComponentObject Model (COM), Distributed Component Object Model (DCOM),Distributed System Object Model (DSOM) and Remote Method Invocation(RMI). The components execute on as few as one computer as in computer802 in FIG. 8, or on at least as many computers as there are components.

Implementation of an Evolvable Synthetic Neural System in a TetrahedralArchitecture

FIG. 11 is a diagram representation of a plurality of autonomic entitiesthat have been assembled to perform a task. These entities can beself-configuring: adapt automatically to the dynamically changingenvironments; self-optimizing: monitor and tune resources automatically;self-protecting: anticipate, detect, identify, and protect againstattacks from anywhere; and, self-healing: discover, diagnose, and reactto disruptions. As shown with reference to autonomic entities 1118 and1120 autonomic computing can have a self-aware layer and an environmentaware layer. The self-aware layer of the autonomic entity (agent orother) can be comprised of a managed component and autonomic manager,which can be an agent, termed a self-managing cell (SMC). Control loopswith sensors (self-monitor) and effectors (self-adjuster) together withsystem knowledge and planning/adapting policies can allow the autonomicentities to be self aware and to self manage. A similar scheme canfacilitate environment awareness—allowing self managing if necessary,but without the immediate control to change the environment; this couldbe affected through communication with other autonomic managers thathave the relevant influence, through reflex or event messages. Theautonomic entities can be arranged or assigned distinctive roles such asworker entities, coordinating or managing entities, and messageentities. Based on the task a ruler entity could be assigned a set ofworker entities to manage inclusive of determining if a stay alivesignal ought to be withdrawn. Further, the communication between theruler and the worker can be facilitated through the message entity. Themessage entity could have the additional task of communicating with aremote system. In the ease of space exploration, the remote system couldbe mission control on earth, mission control on an orbital platform, orany other arrangement that can facilitate that is external to thecollection of autonomic elements. The remote system could be anautonomic entity acting like the project manager for the mission.Communication with mission control will be limited to the download ofscience data and status information. An example of such a grouping isshown in FIG. 11 where autonomic entity 1102 is shown as a ruler entity,autonomic entity 1110 as a message entity, and autonomic entities 1118and 1120 are examples of worker entities. In terms of hardware, theseentities can be all identical with the discernable difference beingprogramming to accomplish assigned tasks. An added advantage to havingidentical hardware is replacing failed entities, which can beaccomplished by activating software code found in the autonomic entity.If hardware differences exist they can be based on specialized equipmentsuitable for a particular task. However, at a minimum, certain functionsor roles, such as ruler and messenger, can be expected to be within theskill set of all the autonomic entities.

As shown in FIG. 11, ruler autonomic entity 1102 can comprise a programor process 1104 executing in ruler entity 1102. Ruler entity 1102 can beimplemented using a data processing system, such as data processingsystem 902 in FIG. 9, or in the form of an autonomous agent compiled bya data processing system. In the alternative, the ruler entity could bean autonomous nano-technology swarm that is launched from a factory shipfor exploring planets, asteroids, or comets. Further, an analysis module1106 or agent as executed by ruler entity 1102 can be used to monitorprocess 1104 and to receive pulse monitor and heart beat monitor signalsfrom worker entities through the messenger entity. When the analysismodule 1106 is used to monitor process 1104 the analysis module 1106 canbe to detect errors or problems with the operation of process 1104.

As shown in FIG. 11, analysis agent 1106 can include an evaluator orother monitoring engine used to monitor the operation of process 1104.Analysis agent 1106 can be executed in response to some event. Thisevent can be a periodic event, such as the passage of some period oftime, data received from one or more of the worker entities. Further,the event can be the initialization of internal procedures in process1104 or the starting or restarting of ruler entity 1102. Depending onthe particular implementation, analysis agent 1106 can continuously runin the background monitoring process 1104 and analyzing the workerentity signals. See method 2100 in FIG. 21 below for actions taken byanalysis agent module 1106 in formulating a strategy for the workerentities. Further, analysis agent 1106 can be subject to anyself-healing routines found in ruler entity 1102.

This monitoring by analysis agent 1106 can be based on rules stored inbehavior storage 1108, which could be used to compare the actualbehavior of the received data to an expected behavior as defined inbehavior storage 1108. In the present arrangement, behavior storage 1108(ruler entity 1102) can be a collection of rules that can be updated bya remote computer through the messenger entity that reflects mostcurrent fixes (self-healing) or repair procedures and responses toworker entities upon the occurrence of an event, change in condition, ordeviation from a normal operation. Behavior storage 1108 can be narrowlytailored based on the use and purpose of the autonomic entity, such asmessenger entity 1110 and have only those procedures needed to performits programming.

When messenger entity connects to remote computer at a command andcontrol station, database 1116 can be updated with information that canlater be used to program ruler entity or worker entity. In most cases acopy of the rules in database 1116 contains the most up-to-dateinformation. If the objective changes or a solution to a problemrequires an updated version not found within the autonomic entity, theentities can attempt to contact message entity 1110 to see if morerecent or up-to-date information is available. If updates are available,these updates can be sent to the requesting entity for processing.

The information in behavior storage 1108 and databases in messenger andworker entity can include an array of values that are expected whenselected process or operations are implemented in the respective entity.Examples processes can be initializing software, timing requirements,synchronization of software modules, and other metrics that can provideinformation concerning the running of a process within the respectiveentity. Examples operations can be data gathering, processing ofinformation, controlling machinery, or any other operation where dataprocessing systems are employed. These expected values can be comparedto determine if an error condition has occurred in the operation of theentity. An error condition can be analyzed to determine its causes andpossible correction. In the case of a worker entity, the error can beinternally analyzed to select the appropriate self-healing procedure andthe error can be sent to the ruler entity to be analyzed by analysisagent 1106 using the rules in behavior storage 1108. Based on theanalysis, the ruler entity can elect to either withdraw the stay alivesignal to the malfunctioning worker entity or wait a selected period togenerate one or more stay alive signal, withdrawal of a stay alivesignal, or a self-destruct signal. If the stay alive signal iswithdrawn, the malfunctioning entity could be disconnected from theoperation and the assigned to another entity or partially performed bythe remaining entity to insure its completion.

FIG. 12 is a block diagram of an autonomous entity management system1200 according to an embodiment. The system 1200 can be a generic systembecause the system 1200 represents a myriad of devices, processes, ordevice and process that perform a task in accordance to its programmingor design. The illustrated system 1200 represents an instance when anautonomous system 1204 encounters an anonymous autonomic agent 1202. Ananonymous autonomous agent can be a visiting agent, a mobile agent thatcan enter the sphere of influence of the autonomous system 1204, or anydevice for which the autonomous system 1204 has no establishedrelationship. Example encounters can be a wireless device (agent) andcommunication tower (system), a client and server, a video subscriberand video provider, a process and an operating system. System 1200manages autonomous entities that can be functionally extracted from anenvironment upon the occurrence of a predetermined condition such as apotential security breach.

The autonomous system 1204 can comprise one or more autonomic agents1208, 1210, and 1212 all performing assigned functions and roles. Asnoted earlier, roles can be a combination of ruler, messenger, andworker. Functions can be data gathering, communication functions,scheduling, controlling, security, and so forth. Upon detectinganonymous autonomic agent 1202 the assigned autonomous agent forperforming security functions for autonomous system 1204 can interrogatethe anonymous autonomic agent 1202, requesting production of validcredentials. Detection can occur by employing various schemes such aswhen the anonymous autonomic agent 1202 requests resources from thesystem 1204 or from any autonomic entity that forms part of the system,response to polling signals from the autonomous system 1204, or througha friend or foe signal that indicates the presence of an anonymousentity 1202 in proximity to the autonomous system 1204.

To the autonomous system 1204, security can be important because ofcompromises by the accidental misuse of hosts by agents, as well as theaccidental or intentional misuse of agents by hosts and agents by otheragents. The result can be damage, denial-of-service, breach-of-privacy,harassment, social engineering, event-triggered attacks, or compoundattacks. To prevent security breaches, visiting agents can be verifiedto have valid and justified reasons for being there as well as providingsecurity to the visiting agent with interaction with other agents andhost. Upon detection the visiting agent 1202 can be sent an asynchronousALice signal (Autonomic license) 1206 requiring valid credentials fromthe agent 1202. The anonymous agent 1202 can need to work within theautonomic system 1204 to facilitate self-management, as such theanonymous agent 1202 and its host can need to be able to identify eachother's credentials through such as an ALice signal. The autonomicsystem 1204 can establish certain response characteristics for thereturned signal from the agent 1202. For example, the autonomic system1204 can require a response in an appropriate format, within a certaintimeout period, and with a valid and justified reason for being withinthe locust of interest or domain of the autonomous system 1204. Forprotection the autonomic system 1204 can make an assessment of thequality of the response from the anonymous agent 1202 to ascertain thepotential of the agent for causing harm to the autonomous system 1204.Based on this determination the autonomous system 1204 can control thetype of interaction with the agent 1202. The agent can be destroyed,blocked, partially blocked, stay alive signal withdrawn, or allowed tocommunicate with other agents within the autonomous system 1204. Theprotection can be triggered at any level of infraction or by acombination of infractions by the anonymous autonomous agent 1202 whenresponding to the ALice signal. If the agent 1202 fails to identifyitself appropriately following an Alice interrogation, the agent 1202can be blocked from the system and given either a self-destruct signal,or its “stay alive” reprieve is withdrawn. A consequence of unacceptableresponse within a timeout period is that the anonymous agent 1202 can beidentified as an intruder or other invalid agent (process) andconsequently, the anonymous agent 1202 is destroyed and/or excluded fromcommunicating with other agents 1208, 1210, 1212 in the system. As analternative to the ALice signal, a quiese signal, command or instructioncan be sent. The quiesce signal is discussed in more detail inconjunction with FIGS. 10, 19 and 20.

FIG. 13 is a hierarchical chart of an autonomous entity managementsystem 1300 according to an embodiment. Properties that a system canpossess in order to constitute an autonomic system are depicted in theautonomous entity management system 1300.

General properties of an autonomic (self-managing) system can includefour objectives defined by International Business Machines 1302:self-configuring 1304, self-healing 1306, self-optimizing 1308 andself-protecting 1310, and four attributes 1312: self-awareness 1314,environment-awareness 1316, self-monitoring 1318 and self-adjusting1320. One skilled in the art will recognize that other properties alsoexist, such as self-quiescing 1324. Essentially, the objectives 1302could represent broad system requirements, while the attributes 1312identify basic implementation mechanisms.

Self-configuring 1304 can represent an ability of the system 1300 tore-adjust itself automatically; this can simply be in support ofchanging circumstances, or to assist in self-healing 1306,self-optimization 1308 or self-protection 1310. Self-healing 1306, inreactive mode, is a mechanism concerned with ensuring effective recoverywhen a fault occurs, identifying the fault, and then, where possible,repairing it. In proactive mode, the self-healing 1306 objective canmonitor vital signs in an attempt to predict and avoid “health” problems(i.e., reaching undesirable situations).

Self-optimization 1308 can mean that the system 1300 is aware of idealperformance of the system 1300, can measure current performance of thesystem 1300 against that ideal, and has defined policies for attemptingimprovements. The system 1300 can also react to policy changes withinthe system as indicated by the users. A self-protecting 1310 system 1300can defend the system 1300 from accidental or malicious external attack,which necessitates awareness of potential threats and a way of handlingthose threats.

Self-managing objectives 1302 can require awareness of an internal stateof the system 1300 (i.e., self-aware 1314) and current externaloperating conditions (i.e., environment-aware 1316). Changingcircumstances can be detected through self-monitoring and adaptationsare made accordingly (i.e., self-adjusting 1320). Thus, system 1300 canhave knowledge of available resources, components, performancecharacteristics and current status of the system, and the status ofinter-connections with other systems, along with rules and policiestherein can be adjusted. Such ability to operate in a heterogeneousenvironment can require the use of open standards to enable globalunderstanding and communication with other systems.

These mechanisms may not be independent entities. For instance, if anattack is successful, this can include self-healing actions, and a mixof self-configuration and self-optimisation, in the first instance toensure dependability and continued operation of the system, and later toincrease the self-protection against similar future attacks. Finally,these self-mechanisms could ensure there is minimal disruption to users,avoiding significant delays in processing.

Other self* properties have emerged or have been revisited in thecontext of autonomicity. We highlight some of these briefly here. Self-*1322 can be self-managing properties, as follows. Self-anticipating isan ability to predict likely outcomes or simulate self-* actions.Self-assembling is an assembly of models, algorithms, agents, robots,etc.; self-assembly is often influenced by nature, such as nestconstruction in social insects. Self-assembly is also referred to asself-reconfigurable systems. Self-awareness is “know thyself” awarenessof internal state; knowledge of past states and operating abilities.Self-chop is the initial four self-properties (Self-Configuration 1304,Self-Healing 1306, Self-Optimisation 1308 and Self-Protection 1310).Self-configuring is an ability to configure and re-configure in order tomeet policies/goals. Self-critical is an ability to consider if policiesare being met or goals are being achieved (alternatively, self-reflect).Self-defining is a reference to autonomic event messages betweenAutonomic Managers: contains data and definition of that data—metadata(for instance using XML). In reference to goals/policies: defining these(from self-reflection, etc.). Self-governing is autonomous:responsibility for achieving goals/tasks. Self-healing is reactive(self-repair of faults) and proactive (predicting and preventingfaults). Self-installing is a specialized form ofself-configuration—installing patches, new components, etc orre-installation of an operating system after a major crash.Self-managing is autonomous, along with responsibility for wider self-*management issues. Self-optimizing is optimization of tasks and nodes.Self-organized is organization of effort/nodes; particularly used innetworks/communications. Self-protecting is an ability of a system toprotect itself. Self-reflecting is an ability to consider if routine andreflex operations of self-* operations are as expected and can involveself-simulation to test scenarios. Self-similar is self-managingcomponents created from similar components that adapt to a specifictask, for instance a self-managing agent. Self-simulation is an abilityto generate and test scenarios, without affecting the live system.Self-aware is self-managing software, firmware and hardware.

FIG. 14 is a block diagram of an autonomic element 1400 according to anembodiment. Autonomic element 1400 can include an element 1402 that isoperably coupled to sensors and 1404 and effectors 1406.

Autonomic element 1400 can also include components that monitor 1408,execute 1410, analyze 1412 and plan 1414; those components can accessknowledge 1416. Those components can interact with sensors 1418 andeffectors 1420.

FIG. 15 is a block diagram of autonomy and autonomicity 1500 at a highsystem level, according to an embodiment. A high level perspective foran intelligent machine design is depicted in FIG. 15. This diagram ofautonomy and autonomicity 1500 includes intelligent machine design andsystem level autonomy and autonomicity.

FIG. 15 describes three levels for the design of intelligent systems:

1) Reaction 1502—the lowest level, where no learning occurs but there isimmediate response to state information coming from sensory systems1504.

2) Routine 1506—middle level, where largely routine evaluation andplanning behaviors take place. Input is received from sensory system1504 as well as from the reaction level and reflection level. This levelof assessment results in three dimensions of affect and emotion values:positive affect, negative affect, and (energetic) arousal.

3) Reflection 1508—top level, receives no sensory 1504 input or has nomotor 1510 output; input is received from below. Reflection is ameta-process, whereby the mind deliberates about itself. Essentially,operations at this level look at the system's representations of itsexperiences, its current behavior, its current environment, etc.

As illustrated, input from, and output to, the environment only takesplace within the reaction 1502 and routine 1506 layers. One can considerthat reaction 1502 level essentially sits within the “hard” engineeringdomain, monitoring the current state of both the machine and itsenvironment, with rapid reaction to changing circumstances; and, thatthe reflection 1502 level can reside within an artificial domainutilizing its techniques to consider the behavior of the system andlearn new strategies. The routine 1506 level can be a cooperativemixture of both. The high-level intelligent machine design can beappropriate for autonomic systems as depicted here in FIG. 15, inconsideration of the dynamics of responses including reaction 1502 andalso for reflection 1508 of self-managing behavior.

As depicted autonomic computing can reside within the domain of thereaction 1502 layer as a result of a metaphoric link with the autonomicbiological nervous system, where no conscious or cognitive activitytakes place. Other biologically-inspired computing (also referred to asnature-inspired computing, organic computing, etc.) can provide suchhigher level cognitive approaches for instance as in swarm intelligence.Within the autonomic computing research community, autonomicity cannotnormally be considered to imply this narrower view. Essentially, theautonomic self-managing metaphor can be considered to aim for auser/manager to be able to set high-level policies, while the systemachieves the goals. Similar overarching views exist in other relatedinitiatives and, increasingly, they are influencing each other.

In terms of autonomy and autonomicity, autonomy can be considered asbeing self-governing while autonomicity can be considered beingself-managing. At the element level, an element can have some autonomyand autonomic properties, since to self-manage implies some autonomy,while to provide a dependable autonomous element requires such autonomicproperties as self-healing along with the elements self-directed task.From this perspective, separation of autonomy and autonomicity ascharacteristics will decrease in the future and eventually will becomenegligible. On the other hand, at the system level if one considersagain the three tiers of the intelligent machine design (reaction 1502,routine 1506, and reflection 1508) and accepts the narrower view ofautonomicity, there is a potential correlation between the levels. Thatis, the reaction 1502 level correlates with autonomicity, and thereflection 1508 level correlates with autonomy; autonomy as inself-governing of the self-managing policies within the system.

FIG. 16 is a block diagram of an architecture of an autonomic element(AE) 1600 according to an embodiment that includes reflection and reflexlayers. The autonomic element 1600 can include a managed component (MC)1602 that is managed, and the autonomic element 1600 can further includean autonomic manager (AM), not shown. The AM can be responsible for theMC 1602 within the AE 1600. The AM can be designed as part of thecomponent or provided externally to the component, as an agent, forinstance. Interaction of the autonomic element 1600 can occur withremote (external) autonomic managers (cf. the autonomic communicationschannel 1606) through virtual, peer-to-peer, client-server or gridconfigurations.

An important aspect of the architecture of many autonomic systems can besensors and effectors, such as shown in FIG. 14. A control loop 1608 canbe created by monitoring 1610 behavior through sensors, comparing thiswith expectations (knowledge 1416, as in historical and current data,rules and beliefs), planning 1612 what action is necessary (if any), andthen executing that action through effectors. The closed loop offeedback control 1608 can provide a basic backbone structure for eachsystem component. FIG. 16 describes at least two control loops in theautonomic element 1600, one for self-awareness 1614 and another controlloop 1608 for environmental awareness.

In some embodiments, the self-monitor/self-adjuster control loop 1614can be substantially similar to the monitor, analyze, plan and execute(MAPE) control loop described in FIG. 14. The monitor-and-analyze partsof the structure can perform a function of processing information fromthe sensors to provide both self-awareness 1614 and an awareness 1608 ofthe external environment. The plan-and-execute parts can decide on thenecessary self-management behavior that will be executed through theeffectors. The MAPE components can use the correlations, rules, beliefs,expectations, histories, and other information known to the autonomicelement, or available to the autonomic element through the knowledgerepository 1416 within the AM 1604.

A reflection component 1616 can perform analysis computation on the AE1600 (cf. the reflection component 1616 within the autonomic manager).In terms of an autonomic system, reflection can be particularly helpfulin order to allow the system to consider the self-managing policies, andto ensure that the policies are being performed as expected. This can beimportant since autonomicity involves self-adaptation to the changingcircumstances in the environment. An autonomic manager communications(AM/AM) component 1618 can also produce a reflex signal 1620. A selfadjuster 1622 can be operably coupled to a self-monitor 1624 in the selfcontrol loop 1614.

Method Embodiments

In the previous section, apparatus embodiments are described. In thissection, the particular methods of such embodiments are described byreference to a series of flowcharts. Describing the methods by referenceto a flowchart enables one skilled in the art to develop such programs,firmware, or hardware, including such instructions to carry out themethods on suitable computers, executing the instructions fromcomputer-readable media. Similarly, the methods performed by the servercomputer programs, firmware, or hardware can also be composed ofcomputer-executable instructions. In some embodiments, methods 1700-2600can be performed by a program executing on, or performed by firmware orhardware that is a part of a computer, such as computer 802 in FIG. 8.

FIG. 17 is a flowchart of a method 1700 to construct an environment tosatisfy increasingly demanding external requirements according to anembodiment where a ruler entity decides to withdraw or generate a stayalive signal. Method 1700 manages autonomous entities that can befunctionally extracted from an environment upon the occurrence of apredetermined condition.

Method 1700 can begin with action 1702 when receiving a signal from amanaged entity. Action 1702 can receive a heart beat monitor (HBM)signal and pulse monitor (PBM) signal from a managed entity such asworker entities 1118 or 1120. The I-IBM signal can be an indication thatthe managed entity (worker entity) is operating. The HBM can be an“ON/OFF” state signal, an indication that a process is being performed,or any other signal that can convey information that the worker entityis alive or active. The PBM signal can extend the HBM signal toincorporate reflex/urgency/health indicators from the autonomic managerrepresenting its view of the current self-management state. The PBMsignal can thus convey the performance and characteristics of the entityin the form of engineering data summarization to add context to thereceived HBM signal. Engineering data summarization can be a set ofabstractions regarding sensor that can comprise rise and fall of data bya certain amount, external causes for parameter deviations, actualnumerical value of the parameters being summarized, warning conditions,alarm conditions, and any other summarization that would convey thegeneral health of the system. Once the HBM and PBM signals have beenreceived, control can be forwarded to action 1704 for furtherprocessing.

In action 1704, an analysis of the HBM and PBM signal can be performedto determine trends and possible areas of concern. Some purposes of theanalysis can be to determine if a predetermined condition is exceeded,to make projection through simulation and data modeling areas ofparameters that can lead to the failure of the worker entity or thatmight jeopardize the assigned mission, and ascertain the quality ofperformance of the system. The analysis can be performed by usingregression techniques, neural network techniques, statisticaltechniques, or any other technique that can convey information about thestate of a system or emergent behavior of the system. Once the analysishas been performed, control can pass to action 1706 for furtherprocessing.

In action 1706, an alarmed condition can be determined. In action 1706,the analysis of action 1704 can be referenced to determine if there isone or more alarm condition that can trigger the withdrawal of a stayalive signal. If no alarm conditions are determined, control can bepassed to action 1708 to generate a stay alive signal. In the event thatan alarm condition is present, control can be passed to action 1710 forfurther processing.

In action 1710, a determination can be performed to ascertain whetherthe identified alarmed condition of action 1706 is recoverable by themanaged entity, such as worker entities 1118 and 1120 of FIG. 11. Whenan alarmed condition is determined to be recoverable, control can bepassed to action 1708 to generate a stay alive signal. When an alarmedcondition is determined not to be recoverable, control can be passed toaction 1712 to withdraw the stay alive signal. Method 1800 below can beone embodiment of determining 1710 if the identified alarmed conditionis recoverable.

FIG. 18 is a flowchart of a method 1800 for ascertaining therecoverability of an alarmed condition determined at action 1706according to an embodiment. Method 1800 manages autonomous entities thatcan be functionally extracted from an environment upon the occurrence ofa predetermined condition. Method 1800 is one possible embodiment of theaction in FIG. 17 above of determining 1710 if the identified alarmedcondition is recoverable.

Method 1800 can begin with action 1802 when receiving one or morealarmed conditions. In action 1802, a determination is performed ofwhether or not an incorrect operation from the managed system has beenidentified in action 1704 of FIG. 17. An incorrect operation can rangefrom not initializing sensors to failing to self-heal when internaldecision logic recommends as an appropriate cause of action. In action1802 in addition to determining if an incorrect operation has beenidentified, the number of devices or processes within the entity thatregistered an incorrect operation can be ascertained. If at least oneincorrect operation is determined, the action can transfer the identityof the unit to evaluation block 1808 for further processing.

In action 1804, a determination is performed of whether or not emergentbehavior from the managed system has been identified in action 1704 ofFIG. 17. An emergent behavior or emergent property can appear when anumber of entities (agents) operate in an environment forming behaviorsthat are more complex as a collective. The property itself can often beunpredictable and unprecedented and can represent a new level of thesystem's evolution. This complex behavior in the context of controlsystem can be known as non-linearity, chaos, or capacity limits. Thecomplex behavior or properties cannot be properties of any single suchentity, nor can they easily be predicted or deduced from behavior in thelower-level entities. One reason why emergent behavior occurs can bethat the number of interactions between autonomic components of a systemincreases combinatorially with the number of autonomic components, thuspotentially allowing for many new and subtle types of behavior toemerge. Nothing can directly command the system to form a pattern, butthe interactions of each part (entities) to its immediate surroundingscan cause a complex process that leads to order. Emergent behavior canbe identified based on parameters that give rise to the complex behaviorin a system such as demands on resources. Once an emergent behaviorcondition has been identified, the information can be forwarded toevaluation block 1808 for further processing.

In action 1806, a determination can be performed of alarm conditionsthat can have an impact on the success of the mission or task by whichall entities are striving to accomplish. The impact could be the abilityto accomplish individual tasks or the potential for failure of theoverall mission by permitting an entity to stay alive. This impact canbe determined through Bayesian belief networks, statistical inferenceengines, or by any other presently developed or future developedinference engine that can ascertain the impact on a particular task ifone or more agent is showing incorrect operation or harmful emergentbehavior. Once the impact has been determined the information can bepassed to evaluation block 1808 for further processing.

Evaluation block 1808 can marshal the incorrect operation identified inaction 1802, the emergent behavior in action 1804, or the effect onmission in action 1806 to suggest a course of action that the managedentities should adopt, which in the present arrangement is based on astay alive signal. The determination of withdrawing or affirming thestay alive signal can be based on the occurrence of one or more of theidentified alarmed conditions, or a combination of two or more of theidentified alarmed conditions. For example, the stay alive signal couldbe withdrawn if there is emergent behavior and there would be an effecton the mission. In the alternative, the stay alive signal could beaffirmed if there was only emergent behavior, or incorrect operation.Once the evaluation is determined, control can be passed to decisionblock 1810 for further processing in accordance to the decision made inevaluation block 1808.

In action 1810, if the desired control instruction is to maintain thestay alive signal, control can be passed to action 1708 for furtherprocessing. In the alternative, a withdrawal of the stay alive signalcan be sent to action 1712 for further processing. Generating a stayalive signal can be equivalent to generating a stay alive signal,affirming a stay alive signal, not withdrawing a stay alive signal, orany other condition that can determine if an entity is to perish or toextinguish unless allowed to continue by another entity. The otherentity might be a managing entity since the other entity can determinethe outcome (life or death) of an entity.

FIG. 19 is a flowchart of a method 1900 to construct an environment tosatisfy increasingly demanding external requirements according to anembodiment where a ruler entity decides to withdraw or generate astay-awake signal. Method 1900 reduces the possibility that an autonomicelement will jeopardize the mission of the autonomic element.

Method 1900 can begin with action 1702 when receiving a signal from amanaged entity. Action 1702 can receive a heart beat monitor (HBM)signal and pulse monitor (PBM) signal from a managed entity such asworker entities 1118 or 1120. In some embodiments, the HBM signal is anindication that the managed entity (worker entity) is operating. The HBMcan be an “ON/OFF” state signal, an indication that a process is beingperformed, or any other signal that can convey information that theworker entity is awake or active. The PBM signal can extend the HBMsignal to incorporate reflex/urgency/health indicators from theautonomic manager representing its view of the current self-managementstate. The PBM signal can thus convey the performance andcharacteristics of the entity in the form of engineering datasummarization to add context to the received HBM signal. Engineeringdata summarization could be a set of abstractions regarding sensorsthat, in some embodiments, could comprise rise and fall of data by acertain amount, external causes for parameter deviations, actualnumerical value of the parameters being summarized, warning conditions,alarm conditions, and any other summarization that would convey thegeneral health of the system. Once the HBM and PBM signals have beenreceived, control can be forwarded to action 1704 for furtherprocessing.

In action 1904, an analysis of the HBM and PBM signal can be performedto determine trends and possible areas of concern. The purpose of theanalysis can be to determine that a predetermined condition has beenexceeded, generate a projection through simulation and data modelingareas of parameters that can lead to the failure of the worker entity orthat might jeopardize the assigned mission, and ascertain the quality ofperformance of the system. The analysis can be performed by usingregression techniques, neural network techniques, statisticaltechniques, or any other technique that can convey information about thestate of a system or emergent behavior of the system. Once the analysishas been performed, control can be passed to action 1706 for furtherprocessing.

In action 1706, an alarmed condition can be determined. In action 1706,the analysis of action 1704 can be referenced to determine if there isone or more alarm condition that can trigger the withdrawal of astay-awake signal. If no alarm conditions are determined, control can bepassed to action 1902 to generate a stay-alive signal. In the event thatan alarm condition is present, control can be passed to action 1904 forfurther processing.

In action 1904, a determination can be performed to ascertain if theidentified alarmed condition of action 1706 is recoverable by themanaged entity such as worker entities 1118 and 1120 of FIG. 11. When analarmed condition is determined not to be recoverable, control can bepassed to action 1712 to withdraw the stay-alive signal. Method 2000below could be one embodiment of determining 1904 if the identifiedalarmed condition is recoverable. When an alarmed condition isdetermined to be recoverable, control can be passed to action 1908 inwhich a determination can be performed to ascertain if quiescing themanaged entity and/or subsequent recovery is possible. When quiescenceof the managed entity and/or need for later recovery is determined asnot possible, control can pass to action 1902 to generate astay-awake/stay-alive-signal. When quiescence of the managed entity isdetermined as possible and/or needed in action 1908, control can pass toaction 1910, to withdraw the stay-awake signal. Thus, quiescing themanaged entity functionally extracts the managed entity from anenvironment upon the occurrence of an alarmed condition. Quiescence canbe a less encompassing alternative to withdrawing the stay-awake signalof apoptosis. Method 1900 can allow an agent or craft that is in dangeror endangering the mission to be put into a self-sleep mode, then laterreactivated or self-destructed.

FIG. 20 is a flowchart of a method 2000 for ascertaining therecoverability of an alarmed condition determined at action 1904. Method2000 manages autonomous entities that can be functionally extracted froman environment upon the occurrence of a predetermined condition.

Method 2000 can begin with action 1802 when receiving one or morealarmed conditions. In action 1802, a determination is performed as towhether or not an incorrect operation from the managed system has beenidentified in action 1704 of FIG. 17. An incorrect operation can rangefrom not initializing sensors to failing to self-heal when internaldecision logic recommends as an appropriate cause of action. In action1802, in addition to determining if an incorrect operation has beenidentified, the number of devices or processes within the entity thatregistered an incorrect operation can be ascertained. If at least oneincorrect operation is determined, the action can transfer the identityof the unit to evaluation block 1808 for further processing.

In action 1804, there can be a determination of emergent behavior fromthe managed system that has been identified in action 1704 of FIG. 17.An emergent behavior or emergent property can appear when a number ofentities (agents) operate in an environment forming behaviors that aremore complex as a collective. The property itself can often beunpredictable and unprecedented and can represent a new level of thesystem's evolution. This complex behavior in the context of controlsystem can be known as non-linearity, chaos, or capacity limits. Thecomplex behavior or properties cannot be properties of any single suchentity, nor can they easily be predicted or deduced from behavior in thelower-level entities. One reason why emergent behavior occurs could bethat the number of interactions between autonomic components of a systemincreases combinatorially with the number of autonomic components, thuspotentially allowing for many new and subtle types of behavior toemerge. Nothing can directly command the system to form a pattern, butinstead the interactions of each part (entities) to its immediatesurroundings can cause a complex process that leads to order. Emergentbehavior can be identified based on parameters that give rise to thecomplex behavior in a system such as demands on resources. Once anemergent behavior condition has been identified, the information can beforwarded to evaluation block 1808 for further processing.

In action 1806, a determination can be performed of alarm conditionsthat can have an impact on the success of the mission or task which allentities are striving to accomplish. The impact could be the ability toaccomplish individual tasks or the potential for failure of the overallmission by permitting an entity to stay awake. This impact can bedetermined through Bayesian belief networks, statistical inferenceengines, or by any other presently developed or future developedinference engine that can ascertain the impact on a particular task ifone or more agent is showing incorrect operation or harmful emergentbehavior, Once the impact has been determined, the information can bepassed to evaluation block 1808 for further processing.

Evaluation block 1808 can marshal the incorrect operation identified inaction 1802, the emergent behavior in action 1804, and the effect onmission in action 1806 to suggest a course of action that the managedentities should adopt, which in the present arrangement is based on astay-awake signal. The determination of withdrawing or affirming thestay-awake signal can be based on the occurrence of one or more of theidentified alarmed conditions, or a combination of two or more of theidentified alarmed conditions. For example, the stay-awake signal couldbe withdrawn if there is emergent behavior and there would be an effecton the mission. In the alternative, the stay-awake signal could beaffirmed if there was only emergent behavior, or incorrect operation.Once the evaluation is determined, control can pass to decision block2002 for further processing in accordance with the decision made inevaluation block 1808.

In action 2002, if the desired control instruction is to maintain thestay-awake signal, control can be passed to action 1902 for furtherprocessing. In the alternative, a withdrawal of the stay-awake signalcan be sent to action 1910 for further processing. Generating astay-awake signal is equivalent to affirming a stay awake signal, notwithdrawing a stay awake signal, or any other condition that candetermine if an entity is to perish or to extinguish unless allowed tocontinue by another entity. The other entity could be a managing entitysince the other entity can determine the outcome (life or death) of anentity.

FIG. 21 is a flowchart of a method 2100 for ascertaining therecoverability of an alarmed condition determined at action 1904. Method2100 manages autonomous entities that can be functionally extracted froman environment upon the occurrence of a predetermined condition.

Method 2100 can begin with action 2102 after having received one or morealarmed conditions. In action 2102, a determination is performed as towhether or not an invalid communication from the managed system has beenidentified in action 1704 of FIG. 17. In action 2102, in addition todetermining if an invalid communication has been identified, the numberof devices or processes within the entity that registered an invalidcommunication can be ascertained. If at least one invalid communicationis determined, the identity of the unit can be transferred to evaluationblock 1808 for further processing. An invalid communication is acommunication handshake that doesn't match an expected protocol, such asthe “rogue” agent didn't respond in the expected manner, or in theexpected time limits, or failed to send a signal in the correct format.

In action 2104, a determination is performed as to whether or not arogue agent from the managed system that has been identified in action1704 of FIG. 17. A rogue agent can exist when a number of entities(agents) operate in an environment forming behaviors that are morecomplex as a collective. One cause of a rogue agent could be that thenumber of interactions between autonomic components of a systemincreases combinatorially with the number of autonomic components, thuspotentially allowing for many new and subtle types of counterproductivebehavior to emerge. Nothing can directly command the system to form apattern, but instead the interactions of each part (entities) to itsimmediate surroundings can cause a complex process that leads to order.A rogue agent can be identified based on parameters that give rise tothe complex behavior in a system such as demands on resources. Once arogue agent has been identified, the information can be forwarded toevaluation block 1808 for further processing.

In action 2106, a determination can be performed of safety/securityissue/concerns that can have an impact on the success of the mission ortask which all entities are configured to accomplish. The impact couldbe the ability to accomplish individual tasks or the potential forfailure of the overall mission by permitting an entity to stay awake.This impact can be determined through Bayesian belief networks,statistical inference engines, or by any other presently developed orfuture developed inference engine that can ascertain the impact on aparticular task if one or more agent is showing invalid communication orharmful rogue agent. Once the safety/security issue/concern has beendetermined, the information can be passed to evaluation block 1808 forfurther processing.

Evaluation block 1808 can marshal the invalid communication identifiedin action 2102, the rogue agent in action 2104, and the safety/securityissue/concern in action 2106 to suggest a course of action that themanaged entities should adopt, which in the present arrangement is basedon a stay-awake signal. The determination of withdrawing or affirmingthe stay-awake signal can be based on the occurrence of one or more ofthe identified alarmed conditions, or a combination of two or more ofthe identified alarmed conditions. For example, the stay-awake signalcould be withdrawn if there is rogue agent and there would be asafety/security issue/concern of the mission. In the alternative, thestay-awake signal could be affirmed if there was only rogue agent, orinvalid communication. Once the evaluation is determined, control canpass to decision block 2002 for further processing in accordance withthe decision made in evaluation block 1808.

In action 2108, if the desired control instruction is not to transmit anotoacoustic signal, control can be passed to action 1902 for furtherprocessing. In the alternative, an otoacoustic signal can be sent inaction 1910 for further processing. The self managing autonomous systemcan self-protect from spurious signals or signals generated by a rogueagent that has failed to engage in a satisfactory ALice exchange bygenerating an otoacoustic An otoacoustic signal is a counteractingsignal to the spurious signals or signals generated by a rogue agentthat is intended to stop the self managing autonomous system fromreceiving, or at least from reacting to, these unwanted signals,effectively having an overriding effect or an equalizing effect on anyreflex signal received by the self managing autonomous system. Inessence, countersignals can be generated that will render theundesirable signals harmless to the self managing autonomous system. Thesecurity and protection of the self managing autonomous system may beimproved by the use of the otoacoustic signal. The otoacoustic signalcan help ensure that self-managing complex systems operate correctlywithout human intervention where management by humans is simply notrealistic or even feasible.

Generating an otoacoustic signal can be equivalent to affirming anotoacoustic signal, not withdrawing an otoacoustic signal, or any othercondition that can determine if an entity is to counteract a spurioussignal or signal from a rogue agent. The other entity could be amanaging entity since the other entity can determine the outcome (lifeor death) of entity.

The present invention may draw inspiration from or have somesimilarities to the mammalian acoustic or stapedius reflex, although oneskilled in the art will recognize that when in danger of exposure toextreme sounds that may damage the ear drum, the mammalian body protectsitself. The acoustic reflex, or stapedius reflex, is an involuntarymuscle contraction in the middle ear of mammals in response tohigh-intensity sound stimuli. The mammalian otoacoustic mechanism,called otoacoustic emission, involves the generation of sound fromwithin the inner ear in response to over-activity of the cochlearamplifier. That is, when the body is presented with a sound that ispotentially damaging, the inner ear generates a counter-sound, which isbenign, and protects the inner ear from hearing it.

In some embodiments, all of the agents, components and apparatus of FIG.1-6 or 11-16 can detect and/or issue the otoacoustic signal, as long asthe agents, components and apparatus are “friendly” (i.e., known not tobe rogue) agents. In some embodiments, however, only a coordinatingagent, such as ruler NBF 608, can perform method 2100.

FIG. 22 is a flowchart of a method 2200 for providing securityrequirements according to an embodiment where a ruler entity decides towithdraw or generate a stay alive signal from an anonymous agent. Method2200 manages autonomous entities that can be functionally extracted froman environment upon the occurrence of a predetermined condition. Method2200 can begin with action 2202 where an ALice signal is sent to ananonymous agent to ascertain the potential for harm of the agent to asystem as shown in FIG. 22. After the ALice signal has been sent to theagent, control can be passed to action 2204 for further processing.

In action 2204 the response from the agent can be monitored. Monitoredas used herein refers to maintaining regular surveillance, or closeobservation, over an anonymous agent and can include the absence of asignal. For example, not responding with a timeout period is considered,as used herein, as monitor response. After action 2204 is completed,control can be passed to action 2206 for further processing.

In action 2206, the monitored response from action 2204 can be analyzedto determine if the monitored response is in an appropriate format,within a certain timeout period, and with a valid and justified reasonfor being within the locust of interest or domain of the autonomoussystem 2204 as shown in FIG. 22. Once the potential for causing harm hasbeen ascertained, control can be passed to action 2208 for furtherprocessing.

In action 2208, the system can control the future of the anonymous agentbased on the potential for harm to the autonomous system. This mimicsthe mechanism of cell death in the human (and animal) body, and hencemakes use of autonomic and other biologically inspired metaphors. Thetechnique would send self-destruct signals to agents that can becompromised, or which cannot be identified as friendly or as having aright to access certain resources. The concept of the ALice signal is tochallenge a mobile agent to determine if the mobile agent is friendlyand to determine if the mobile agent has permission to access certainresources. If the mobile agent fails to identify itself appropriatelyfollowing an ALice interrogation, the mobile agent can be blocked fromthe system and given either a self-destruct signal, or its stay alivereprieve is withdrawn. As an alternative to the ALice signal, a quiescesignal, command or instruction can be sent. The quiesce signal isdiscussed in more detail in conjunction with FIGS. 10, 19 and 20.

FIG. 23 is a flowchart of a method 2300 of autonomic communication by anautonomic element. Method 2300 can offer a holistic vision for thedevelopment and evolution of computer-based systems that brings newlevels of automation and dependability to systems, while simultaneouslyhiding their complexity and reducing their total cost of ownership.

Method 2300 can include transmitting self health/urgency data 2302.Examples of the self health/urgency data can include informationdescribing low battery power and/or failed sensors. Method 2200 can alsoinclude transmitting 2304 environment health/urgency data. Examples ofthe environment health/urgency data can include information describinginaccessible devices, unauthorized access, and/or an unidentified mobileagent sending communication signals.

Transmitting 2302 and 2304 can be performed in any order relative toeach other. For example, in one embodiment the transmitting 2302 selfhealth/urgency data can be performed before transmitting 2304environment health/urgency data. In another embodiment, transmitting2304 environment health/urgency data can be performed beforetransmitting 2302 self health/urgency data. In yet another embodiment,the self health/urgency data can be transmitted simultaneously with theenvironment health/urgency data. For example, the environmenthealth/urgency data and the self health/urgency data can be transmittedtogether. One example of transmitting the environment health/urgencydata and the self health/urgency data can include encapsulating theenvironment health/urgency data and the self health/urgency data in aX.25 packet, although one skilled in the art will readily recognize thatany number of alternative packet types can be used that fall within thescope of this invention. The environment health/urgency data and theself health/urgency data can be thought of together as the “lub-dub” ofa heartbeat in which the two “beats” or two pieces of data aretransmitted simultaneously. The X.25 standard is published by the ITUTelecommunication Standardization Sector at Place des Nations, CH-1211Geneva 20, Switzerland.

An autonomic environment can require that autonomic elements and, inparticular, autonomic managers communicate with one another concerningself-* activities, in order to ensure the robustness of the environment.A reflex signal 1620 of FIG. 16 above can be facilitated through thepulse monitor (PBM). A PBM can be an extension of the embedded system'sheart-beat monitor, or HBM, which safeguards vital processes through theemission of a regular “I am alive” signal to another process with thecapability to encode self health/urgency data and environmenthealth/urgency data as a single pulse. HBM is described in greaterdetail in FIGS. 14 and 21 above. Together with the standard eventmessages on an autonomic communications channel, this can providedynamics within autonomic responses and multiple loops of control, suchas reflex reactions among the autonomic managers. Some embodiments ofthe autonomic manager communications (AM/AM) component 1618 can producea reflex signal 1620 that includes the self health/urgency data and theenvironment health/urgency data in addition to the HBM. More concisely,the reflex signal can carry a PBM. A reflex signal that carries a PBMcan be used to safe-guard the autonomic element by communicating healthof the autonomic element to another autonomic unit. For instance, in thesituation where each PC in a LAN is equipped with an autonomic manager,rather than each of the individual PCs monitoring the same environment,a few PCs (likely the least busy machines) can take on this role andalert the others through a change in pulse to indicate changingcircumstances.

An important aspect concerning the reflex reaction and the pulse monitoris the minimization of data sent—essentially only a “signal” istransmitted. Strictly speaking, this is not mandatory; more informationcan be sent, yet the additional information should not compromise thereflex reaction.

Just as the beat of the heart has a double beat (lub-dub), the autonomicelement's pulse monitor can have a double beat encoded—as describedabove, a self health/urgency measure and an environment health/urgencymeasure. These match directly with the two control loops within the AE,and the self-awareness and environment awareness properties.

FIG. 24 is a flowchart of a method 2400 of autonomic communication by anautonomic element. Method 2400 can include transmitting 2402 eventmessage data in addition to the self and environment health/urgencydata. Event message data can include data describing a change incondition, or a deviation from a normal operation. Event message data isdescribed in more detail above in FIG. 11.

In some embodiments, the self health/urgency data and environmenthealth/urgency data encoded with the standard event messages on anautonomic communications channel, can provide dynamics within autonomicresponses and multiple loops of control, such as reflex reactions amongan autonomic manager.

FIG. 25 is a flowchart of a method 2500 of autonomic communication by anautonomic element. Method 2500 can include receiving 2502 the selfhealth/urgency data from a self control loop component of the autonomicelement. One example of the self control loop component of the autonomicelement can be the self awareness control loop 1614 of the autonomicelement 1600 of FIG. 16 above.

Method 2500 can also include receiving 2504 the environmenthealth/urgency data from an environment control loop component of theautonomic element. One example of the environment control loop componentof the autonomic element can be the environment awareness control loop1608 of the autonomic element 1600 of FIG. 16 above.

FIG. 26 is a flowchart of a method 2600 of autonomic communication by anautonomic element. Method 2600 can offer a holistic vision for thedevelopment and evolution of computer-based systems that brings newlevels of automation and dependability to systems, while simultaneouslyhiding their complexity and reducing processing delays by systems thatreceive data from the autonomic element.

Method 2600 can include transmitting uncompressed self health/urgencydata 2602. Method 2600 can also include transmitting 2604 uncompressedenvironment health/urgency data. In the absence of bandwidth concerns,the uncompressed data can be acted upon quickly and not incur processingdelays. One important aspect can be that the data, whether uncompressedor sent in some other form, should be in a form that can be acted uponimmediately and not involve processing delays (such as is the case ofevent correlation). Transmitting 2602 and 2604 can be performed in anyorder relative to each other.

An otoacoustic component of an autonomic nit can render an incomingpotentially harmful signal inert. Self-managing systems, whether viewedfrom the autonomic computing perspective, or from the perspective ofanother initiative, can offer a self-defense capability that brings newlevels of automation and dependability to systems, while simultaneouslyhiding their complexity and reducing their total cost of ownership.

According to various embodiments, a number of initiatives inspired byconcepts from biology have arisen for self-management of a complexsystem. Biological systems, and in particular, the Autonomic NervousSystem (ANS), are capable of performing autonomic, innate or in-built,self-regulation activities requiring no conscious thought. In similarfashion, and according to various embodiments of the present teachings,a software system is provided that manages itself. In some embodiments,the system takes advantage of emergent behavior similar to that insocial insect colonies. It has been found that emergent behavior helpsan insect colony to collectively solve complex problems withoutcentralized control. Thus, colony (or swarm) behavior appears out oflocal interactions between individuals with simple rule sets and noglobal knowledge. In fact, emergent behavior does not “help” insectcolonies in the general sense of the word, but instead it is thecoordinated behavior of the social insects that collectively solvesproblems. What is emergent in this example is coherence and cooperationfrom a global point of view, where at the level of the individualnothing actively pushes for it.

According to various embodiments, the self-sacrifice behavior of one ormore individual components can be absorbed in se the usefulness or livesof other individual components. Thus, an emergent behavior is providedwherein the individual components sacrifice themselves to jointly solvea complex problem vital to the entire system or swarm. In someembodiments an emergent behavior can be provided by the system wherebyone or more components of the system self-sacrifices itself for thegreater good of the system. In an example, each component can beprogrammed to identify at least one condition that would cause therespective component to be detrimental to the greater good of thesystem. The component could also be programmed to self-sacrifice itselfif the condition is determined to exist, thereby benefiting the entiresystem. As an illustration, such a time-to-self-sacrifice condition cancomprise exceeding a risk threshold, for example, a threshold determinedby a risk analysis program that is run periodically during operation ofthe system component. For example, if the continued operation of thecomponent poses a risk of collision with another component of thesystem, and the risk exceeds a risk threshold, a self-sacrificeoperation can be initiated for the greater good of the entire system.Self-sacrifice can comprise, for example, shutting down,self-destruction, or the like. Self-destruction can be, for example, byexplosion, by implosion, or be steering into an asteroid, planet, orsun, in the case of a multiple spacecraft system.

According to various embodiments of the present teachings, an autonomicnano technology swarm (ANTS) system is provided. With the ANTS system, aswarm of small autonomous exploration vehicles, such as spacecraft, canbe used for an exploration mission, thus reducing the costs and risksinvolved when only a single, larger spacecraft is used. The systemfurther enables exploration missions where a single, large, spacecraftwould be impractical, and can offer greater redundancy and increasedmission longevity in harsh environments. The ANTS system can exhibit allof the features of a multi-agent autonomic system (AS) wherein thespacecraft, vehicles, or system components themselves are autonomicelements (AEs).

According to various embodiments, the software architecture is adaptivein all its attributes and functionality, including its performance,security, fault tolerance, configurability, and the like. Moreover, thesystem can make decisions to cope with new environmental conditionsencountered, and can learn and evolve to become better adapted to whatit is supposed to do. Thus, a spacecraft unit can be programmed suchthat its own self-sacrifice can be used to protect other components orunits vital to the system, or programmed to self-sacrifice if the resultwould be a significant performance gain for the entire system ormission.

In some embodiments, the emergent behavior is a complex behavior derivedspontaneously from simple rules. Thus, the emergent behavior can enablethe production of a high-level, more complex behavior through theinteraction of multiple system components, by following simple rules.

The self-sacrifice behavior described herein differs from the otherapproaches mentioned above in at least the following ways. Firstly, insome embodiments, “death” is not always assumed for the individualcomponent. Secondly, in some embodiments, self-sacrifice can comprisemaking a choice of leaving a critical task to another individualcomponent when the individual's own performance is not optimal. Theseand other advantages are apparent from the present teachings.

According to various embodiments, an autonomic element in a systemcomprising numerous autonomic elements can exhibit or be programmed toexhibit self-adapting behavior to improve performance and/or to protectvital parts of the system. According to some embodiments, an autonomicelement can comprise a spacecraft in an autonomous space mission.According to some embodiments, the spacecraft can comprise an autonomousworker vehicle or component in an autonomous space mission which missioninvolves using a plurality of different spacecrafts. According to someembodiments, the autonomic space mission can be performed by an ANTSsystem as described above. According to some embodiments, eachspacecraft in the ANTS system can have a specialized mission. Accordingto some embodiments, individual components, such as spacecraft or unitsin the ANTS system, can be programmed to exhibit emergent self-adaptingbehavior. An individual spacecraft unit of the system can performself-sacrifice as part of its self-adapting behavior, in order toimprove the system performance and/or to protect vital parts of thesystem, for example, based on goals of an exploration mission.

According to various embodiments, the system uses Autonomic SystemSpecification Language (ASSL). The ASSL can be used to model theself-sacrifice behavior of the individual spacecraft units. The ASSL canfollow simple predefined rules, but can help in the formation of anemergent complex system-level behavior that strives to protect andoptimize the system as a whole. It should be understood that byself-sacrifice, “death” is not the only option under consideration forthe spacecraft, but rather, in some embodiments another option forself-sacrifice can comprise a voluntary relinquishment from the “socialstatus” of the spacecraft in the swarm. According to some embodiments,the voluntary relinquishment from the “social status” can be achieved bydelegating rights from one component to another, for example, from onespacecraft to another spacecraft of the system.

The systems, clients, servers, methods, computer-readable media,software, hardware, and operating environments that can be used includethose described in U.S. Patent Applications Publications Nos. US2007/0073631 A1, entitled “Systems, Methods and Apparatus for Quiescenceof Autonomic Systems,” and US 2007/0260570 A1, entitled “Systems,Methods and Apparatus for Autonomic Safety Devices,” which areincorporated herein in their entireties, by reference.

ANTS Structure

According to various embodiments of the present teachings, an ANTSsystem is provided for use in a sub-mission Prospecting AsteroidsMission (PAM). The PAM can provide a novel approach to asteroid beltresource exploration. ANTS can provide extremely high autonomy, minimalcommunication requirements to Earth, and a set of very small explorerswith few consumables. In some embodiments, the explorers forming theswarm can be pica-class, low-power, and low-weight spacecraft units, yetcapable of operating as fully autonomous and adaptable agents.

FIG. 27 depicts a PAM sub-mission scenario of the ANTS concept mission,according to various embodiments of the present teachings. As depictedin FIG. 27, a transport spacecraft launched from Earth toward anasteroid belt can carry a laboratory that assembles tiny spacecraft.Once the transport spacecraft reaches a certain point in space wheregravitational forces are balanced, termed a Lagrangian, and in this casethe L1 Lagrangian point, the transport ship can release the assembledswarm, which can head toward the asteroid belt. Each spacecraft can beequipped with a solar sail and thereby can rely primarily on power fromthe sun, using, for example, tiny thrusters to navigate independently.

As FIG. 271 shows, there can be at least three classes of spacecraft;coordinating autonomic components (rulers); messenger autonomiccomponents (messengers); and autonomic worker components (workers). Bygrouping them in appropriate ways, the ANTS system can form teams thatexplore particular asteroids of the asteroid belt. Hence, the ANTSsystem can exhibit self-organization since there is no external forcedirecting its behavior and no single spacecraft having a global view ofthe intended macroscopic behavior. According to some embodiments, theinternal organization of the swarm can depend on the global task to beperformed and on the current environmental conditions. According to someembodiments, the swarm can consist of several sub-swarms, which can betemporal groups organized to perform a particular task. According tosome embodiments, each sub-swarm can have a coordinating group leader(ruler), one or more messengers, and a number of workers each carryingat least one specialized instrument. According to some embodiments, themessengers can connect or provide communications between the teammembers when such team members cannot connect directly to one another.

Self-Sacrifice Scenarios in ANTS

According to various embodiments, the system can implementself-optimization. In general, the global system optimization can becorrelated to the optimization of the individual system elements. Eachcomponent of the system can prove its performance on-the-fly. Forexample, in some embodiments rulers can use experience gained toself-optimize. As an example, rulers can use their experience to improvetheir ability to identify asteroids. In some embodiments, messengers canstrive to find the best position to improve communication among theother components or swarm units. According to some embodiments, workerscan self-optimize through learning and experience.

Single components can “die” for the good of the entire system orotherwise self-sacrifice, for example, by voluntarily relinquishingtheir posts. In some embodiments, a spacecraft unit can, for example,voluntarily relinquish its post as an ANTS worker component bydelegating tasks to other worker components. While scenarios related toself-sacrifice of ANTS workers are described in great detail herein, itis to be understood that other classes of spacecraft, for example,rulers and/or messengers, can also be configured to self-sacrificevoluntarily.

According to various embodiments, a worker can “die” or sacrifice itselfvoluntarily. For example, a worker can sacrifice itself voluntarily whenthe worker cannot continue performing its duties as a worker. As anillustration, a worker can self-sacrifice if it cannot continue tosupport the service-level objectives assigned to it (for example, if itcannot achieve performance). While operating in space, for example, aninstrument of a worker can be damaged but not destroyed. For example,the instrument may still be operational, but its performance might bedegraded or destroyed. According to some embodiments, a worker with adestroyed or heavily damaged instrument that performs below aperformance minimum, can self-sacrifice voluntarily.

According to some embodiments, the self-sacrifice can comprise atransformation, for example, a transformation of a worker. If a workercannot perform its duties anymore, due to a damage or instrument loss,the worker can, according to various embodiments, perform one or moreoperations. The operations can comprise, for example, asking the rulerto assign a new replacement worker, and/or striving to transform intoanother category of component useful to the swarm unit. Suchtransformation can comprise, for example, transforming from a worker toa messenger, from a messenger to a worker, from a worker to a ruler, orthe like. According to some embodiments, a worker can try to transformto a ruler or a messenger, but if it is not possible for the worker totransform to a ruler or messenger, the worker can instead transform to ashield component such as a stand-by shield. According to someembodiments, such a shield component can sail nearby and strive toprotect the replacement worker from different hazards. For example, ashield unit can, according to some embodiments, take the impact of anincoming small asteroid which is about to hit the replacement worker.The shield unit does not have to spend additional time and resources torecover from this probable impact. This kind of protection can comprisea complete self-sacrifice because the shield unit can serve as suchuntil its full destruction, while increasing the overall performance ofthe system.

According to various embodiments, the self-sacrifice operation cancomprise a self-destruction operation. According to some embodiments,when a worker is damaged so badly that it cannot move anymore, theworker can self-destruct, for example, by exploding. This can be used toavoid the risk of collision with another component of the system. Hence,there can be a real self-sacrifice that indirectly leads to highersystem performance, due to the reduction in the risk of an impact.

According to various embodiments, the self-sacrifice behavior of asingle component, such as a spacecraft unit in an ANTS system, can bemodeled with Autonomic System Specification Language (ASSL). With ASSL,validation and code generation of specified instructions can beachieved. According to various embodiments, the ASSL can be definedthrough formalization tiers. According to some embodiments, over theformalization tiers, ASSL can provide a multi-tier specification modelthat is designed to be scalable and to expose a judicious selection andconfiguration of infrastructure elements and mechanisms needed by an AS.ASSL can define an AS with interaction protocols and AEs, where the ASSLtiers and their sub-tiers describe different aspects of the AS underconsideration, like policies, communication interfaces, executionsemantics, actions, and the like.

According to various embodiments, the ASSL tiers and their sub-tiers, asshown in FIG. 28, can be abstractions of different aspects of theautonomic system under consideration. According to some embodiments, theAS Tier can specify an AS in terms of service-level objectives (AS SLO),self-management policies, architecture topology, actions, events, andmetrics. According to some embodiments, the AS SLO can be a high-levelform of behavioral specification that establishes system objectives suchas performance. According to some embodiments, the self-managementpolicies of an AS can include: 1) self-configuring; 2) self-healing; 3)self-optimizing; and 4) self-protecting (also referred to herein as aself-CHOP of an AS). Other self-management policies can also or insteadbe included. According to some embodiments, the metrics can constitute aset of parameters and observables controllable by the AEs.

According to various embodiments, at the AS Interaction Protocol tier,the ASSL framework can specify an AS-level interaction protocol (ASIP).According to some embodiments, AS EP can be a public communicationinterface, expressed as communication channels, communication functions,and messages.

According to various embodiments, at the AE Tier, the ASSL formal modelconsiders AEs to be analogous to software agents able to manage theirown behavior and their relationships with other AEs. According to someembodiments, at the AE Tier, ASSL can describe the individual AEs.

According to various embodiments, a worker's self-sacrifice behavior canbe modeled with ASSL. It should be understood that the model presentedand described herein is exemplary only.

According to various embodiments, the self-sacrifice behavior cancomprise a self-management policy, which can be specified at theindividual component or spacecraft level (at the AE Tier). FIG. 28presents a partial specification of a self-sacrifice policy that can beused according to various embodiments based on the scenarios describedherein. FIG. 28 describes possible choices a worker can make when theworker is no longer minimally or fully operational. According to variousembodiments, the definitions that follow can be used to specifyexemplary the self-sacrifice policies.

Self-sacrifice can be defined as a self-management policy structure. Aset of fluents and mappings can be used to specify this policy. Withfluents, specific situations can be expressed, in which the policy isinterested. With mappings, the situations can be mapped to actions.

Actions can be defined as a set of actions that can be undertaken by theworker in response to certain conditions, and according to that policy,as shown in FIG. 29.

Events can be defined as a set of events that initiate fluents and canoptionally be prompted by actions according to that policy.

Metrics can be defined as a set of metrics needed by that policy.

According to various embodiments, the unableToExplore fluent, shown inFIG. 28, can take place when the worker is no longer operational, dueto, for example, heavy damage or instrument loss. The fluent can beinitiated by an instrIsNonfunctional event and can terminate if one ofthe events canBeRuler, canBeMessenger, canBeShield, or mustBeDestroyedoccurs. In some embodiments, this fluent can be mapped to acheckTransformation action that checks for a possible workertransformation and triggers one of the triggering events that terminatethe current fluent. According to some embodiments, each of theterminating events can initiate a new fluent respectively. According tosome embodiments, the “transform” fluents, shown in FIG. 28, can bemapped to “transformTo” actions, exemplary portions of which arepresented in FIG. 29. The mapping can transform the worker into a ruler,a messenger, or a shield, according to the example shown. As specified,the transformation attempts can be hierarchically related. Thus, whenpossible, the transformation process can start with a transformationinto a ruler or into a messenger, and then, in case of failure, thealgorithm can attempt to perform a transformation into a shield.According to some embodiments, at the end of the hierarchically orderedtransformations, self-destruction of the worker can be performed, incase none of the transformations is successful. A self-destructiondevice can be included in or on the component, for example, an explosivecharge and appropriate detonation circuitry mounted in a spacecraft.

According to various embodiments. ASSL can allow specification ofsystems evolving over time. According to some embodiments, the evolutionof such systems can take place in the actions of the system. Accordingto some embodiments, via a finite set of change, remove, add, and createstatements, the actions of the system can prompt changes in the tiersand sub-tiers of the AS under consideration.

FIG. 30 presents a partial specification of some of the actions whichcan be needed by the self-sacrifice policy. It should be understoodthat, the “transformTo” actions can change the service-level objectives(SLO) of the worker under consideration. According to some embodiments,these actions can be used to re-specify the component or unit inaccordance with the new SLO. According to some embodiments, thetransformToShield action can first remove the old worker SLOspecifications and next create the new shield SLO, as shown in FIG. 3,thus avoiding contradictions between both worker and shield SLO. Itshould be understood that while the add statements in FIG. 3 are onlypartially their use in a more complex algorithm would be apparent tothose skilled in the art given the present teachings.

According to some embodiments, the transformToShield action can turn offthe other worker's self-management policies to avoid contradictionsbetween both worker and shield SLO. According to some embodiments, thetransformToShield action can turn off the other worker's self-managementpolicies via four change statements, which set the SWITCH flag of theself-management policies to OFF.

In some embodiments, the physical transformation can be accomplished bythe IMPL routine doShieldTransformation. The IMPL clause states “forfurther implementation”. This means that the ASSL framework willgenerate doShieldTransformation as an empty routine for manualimplementation.

While spacecraft have been exemplified herein as the system components,it is to be understood that other systems and components are also withinthe scope and spirit of the present teachings. Systems comprising deepsea exploration components, land-based exploration components,atmospheric-based exploration components, or other exploration vehicles,should also be considered to be within the realm of the presentteachings.

According to the present teachings, a self-managing computer system hasbeen developed based on autonomic computing. The autonomic computingsystem is analogous to the biological nervous system, whichautomatically maintains homeostasis (metabolic equilibrium) and controlsresponsiveness to external stimuli. For example, most of the time ahuman is not consciously aware of its breathing rate or how fast itsheart is beating, although if the human touches a sharp knife with itsfinger the result is a reflex reaction to move the finger out of danger.If the human cuts itself and starts bleeding, the wound can be treatedand the human can then carry on without thinking about it, although painreceptors will induce self-protection and self-configuration to use theother hand. Yet, often the cut will have caused skin cells to bedisplaced down into muscle tissue. If the cells survive and divide, theyhave the potential to grow into a tumor. The human body's solution tothis situation is cell self-destruction. There is mounting evidence thatsome forms of cancer are the result of cells not dying fast enough,rather than multiplying out of control, as previously thought.

Biologists believe that cells are programmed to commit suicide through acontrolled process known as apoptosis. The term is derived from theGreek word for “to fall off,” in reference to dead leaves falling fromtrees in autumn. Likewise, cells “fall off” living organisms and die. AsFIG. 31 shows, a cell's constant receipt of “stay alive” signals turnsoff the self-destruct sequence. Biological apoptosis is shown in FIG.31A, which shows that when a cell constantly receives “stay alive”signals it turns off its programmed self-destruct sequence. FIG. 31B, onthe other hand, shows apoptosis versus necrosis due to an injury. Whenthese signals cease, the cell starts to shrink, internal structuresdecompose, and all internal proteins degrade; thereafter, the cellbreaks into small, membrane-wrapped fragments to be engulfed byphagocytic cells for recycling. FIG. 31B contrasts apoptosis, also knownas “death by default,” with necrosis, which is the unprogrammed death ofa cell due to injury, inflammation, and the accumulation of toxicsubstances.

Autonomic Agents

Autonomic computing can depend on many disciplines for its success,including, for example, research in agent technologies. There are noassumptions that an autonomic architecture must use agents, but agentproperties complement the objectives of the paradigm. The propertiesthat can be utilized in forming an autonomic computing system accordingto the present teachings can include, for example, adaptability,autonomy, cooperation, and the like. In addition, complex systems can beformed with multiple agents, and in such embodiments, the systems cancomprise inbuilt redundancy and greater robustness, and can beretrofitted in legacy systems with autonomic capabilities that maybenefit from an agent-based approach.

Referring back to FIG. 16, an autonomic computing system is shownaccording to the present teachings, which comprises a basic autonomicelement (AE) that consists of a managed component (MC) and an autonomicmanager (AM). The AM can be a stationary agent, for example, aself-managing cell that contains functionality for measurement and eventcorrelation and provides support for policy-based control. The AMs cancommunicate through an autonomic channel via means such as self-* eventmessages. The AM

AM communications module includes heartbeat monitoring and pulsemonitoring.

Mobile agents can be utilized in the autonomic systems of the presentteachings. Their ability to reduce network load, overcome networklatency, encapsulate protocols, execute asynchronously and autonomously,adapt dynamically, reflect natural heterogeneity, and maintainrobustness and fault tolerance, can make it easier for AMs withindifferent systems to cooperate.

Apoptosis in Agent-Based Autonomic Environments

In Greenberg et al., “Mobile Agents and Security,” IEEE Comm. Magazine,July 1998, pp. 76-85, agent destruction to facilitate security inmobile-agent systems is described, and the publication is incorporatedherein in its entirety by reference. The article describes a scenario inwhich mobile agents—not rogue agents, but instead ones carrying properauthenticated credentials—carried out work that was out of contextrather than the result of abnormal procedures or system failure. In thiscircumstance, the mobile agents could cause substantial damage. Forexample, the mobile agents could deliver an archaic upgrade to part of anetwork operating system, bringing down the entire network. These andother misuses involving mobile agents can occur in several forms. Agentscan accidentally or unintentionally misuse hosts due to, say, raceconditions or unexpected emergent behavior in those agents. In addition,external bodies acting upon agents, either deliberately or accidentally,can lead to their misuse by hosts or other agents. Misuses can result,for example, due to damage, breaches of privacy, harassment, socialengineering, event-triggered attacks, or compound attacks.

Encryption can prevent situations in which portions of an agent's binaryimage could be copied when visiting a host, for example, portions suchas monetary certificates, keys, information, and the like. Agentexecution, however, requires decryption, which provides a window ofvulnerability. This situation is analogous to the body's vulnerabilityduring cell division.

FIG. 32 shows a high-level view of a simple, autonomic environment withthree autonomic elements (AEs). It is to be understood, however, thatsystems having hundreds, thousands, or even millions of AEs are withinthe scope of the present teachings. Each AE shown in FIG. 32 is anabstract view of FIGS. 31A and 3113, and in this case the MCs representself-managing computer systems. These AEs can have many otherlower-level AEs, for example, an autonomic manager for the disk drive,while at the same time residing within the scope of a higher-level AM:such as a system-wide local area network domain's AE. In variousembodiments, the self-managing computer systems can comprise andautonomic and apoptotic cloud computing system, an autonomic andapoptotic grid computing system, an autonomic and apoptotic highlydistributed computing system, a combination thereof, or the like.

Within each AM, heartbeat monitors (HBMs) send “I am alive” signals toensure the continued operation of vital processes in the MC and toimmediately indicate if any fail. The AM has a control loop thatcontinually monitors and adjusts, if necessary, metrics within the MC,yet vital processes in the MC can also be safeguarded by an HBM thatemits a heartbeat signal as opposed to its being polled by the AM,avoiding lost time (time to next poll) by the AM to notice a failure.Note that in FIG. 32 the left-hand AE has an HBM between the AM and aprocess on the MC. Because each AM is aware of its MC's health via thecontinuous control loop, it can share this information by sending apulse signal (“I am tin/healthy”) to another AM, for example, from theleft-hand AE to the middle AE as shown in FIG. 32. This not only allowsself-managing options if the machines are, for example, sharing workloadas a cluster, but also protects the AM itself as the pulse signal alsoacts as an HBM signal from one AM to another. Thus, if an AE's vitalprocess fails, the neighboring AM will immediately become aware of itand, for example, try to restart the failed AE or initiate a failover toanother AM. This pulse signal can also act as a reflex signal betweenAMs warning of an immediate incident, which is a more direct solutionthan having the AM process numerous event messages to eventuallydetermine an urgent situation.

Because AMs also monitor the external environment (the second controlloop), they have a view of the health of their local environment. Theycan encode such information into the pulse signal along with self-healthdata (just as our hearts have a double beat). The double-pulse signalsbetween the right-hand and center AEs in FIG. 32 represent thissituation.

In some embodiments, AMs can dispatch mobile agents to work on theirbehalf, for example, to update a set of policies. To help provideself-protection in these situations, AMs can send apoptosis signals(“stay alive/self-destruct”) to such agents by either authorizingcontinued operation or by withdrawing such authorization. An example ofwhen authorization for continued operation can be withdrawn can include,for instance, when policies become out of date. FIG. 32 depicts bothscenarios.

The absence of a “stay alive” signal resulting in agent self-destructioncan be referred to as strong apoptotic computing, or programmed death bydefault, while weak apoptotic computing can involve an explicitself-destruct signal. The differences in these approaches are subtle butimportant. Only a built-in default death can guarantee true systemsafety. For example, you would never rely on a self-destruct signalgetting through to an agent containing system password updates in ahostile environment. Likewise, a robot with adaptive capabilities couldlearn to ignore such a signal. Not all circumstances require adeath-by-default mechanism, however, many researchers using programmeddeath under the apoptosis descriptor can use programmed death bydefault.

There is a concern that denial-of-service attacks could prevent “stayalive” signals from reaching their target and thereby induceunintentional agent self-destruction. DoS attacks could likewiseinterrupt terminate signals, resulting in potentially dangerousscenarios. DoS-immune architectures can thus be useful in theself-managing systems of the present teachings.

Swarm Space Exploration Systems

Space exploration missions by necessity have become increasinglyautonomous and adaptable. To develop more self-sustainable explorationsystems, the present teachings provide the use of biologically inspiredswarm technologies. Swarms of small spacecraft are used and offergreater redundancy, greater protection of assets, lower costs, lowerrisks, and the ability to explore more remote regions of space, whencompared to a single large craft. Such a new space exploration paradigmcalls for missions involving many, for example, thousands of, smallspacecraft rather than a single large craft.

The Autonomous NanoTechnology Swarm mission, a.k.a. ANTS,(http://ants.gsk.nasa.gov), is a collaboration between NASA's GoddardSpace Flight Center and its Langley Research Center, and exploits swarmtechnologies and artificial intelligence (AI) techniques to developrevolutionary architectures for both space craft and surface-basedrovers. ANTS consists of several submissions: the Saturn Autonomous RingArray (SARA); the Prospecting Asteroid Mission (PAM); and the LanderAmorphous Rover Antenna (LARA).

The Saturn Autonomous Ring Array consists of a swarm of 1,000 pico-classspacecraft, organized as 10 subswarms with specialized instruments, toperform in situ exploration of Saturn's rings to better understand theirconstitution and how they were formed. SARA uses self-configuringstructures for nuclear propulsion and control as well as autonomousoperation for both maneuvering around Saturn's rings and collisionavoidance.

The Prospecting Asteroid Mission (PAM) also involves 1,000 pico-classspacecraft but with the aim of exploring the asteroid belt andcollecting data on particular asteroids of interest for potential futuremining operations.

The Lander Amorphous Rover Antenna (LARA) implements new NASA-developedtechnologies in the field of miniaturized robotics, to form the basis ofremote lunar landers launched from remote sites, as well as offeringinnovative techniques to allow rovers to move in an amoeboid fashionover the moon's uneven terrain.

The ANTS architecture emulates the successful division of laborexhibited by low-level social-insect colonies. In such colonies, withsufficiently efficient social interaction and coordination, a group ofspecialists usually outperforms a group of generalists. To accomplishtheir specific mission goals, ANTS systems likewise rely on largenumbers of small, autonomous, reconfigurable, and redundant worker craftthat act as independent or collective agents. The architecture isself-similar in that ANTS system elements and subelements can bestructured recursively, and it is self-managing, with at least one ruler(AM) per ANTS craft. An exemplary system is shown in FIGS. 27-30.

NASA missions such as ANTS provide a trusted private environment,eliminating many agent security issues and enabling system designers tofocus on ensuring that agents are operating in the correct context andexhibiting emergent behavior within acceptable parameters.

In considering the role of the self-destruct property inspired byapoptosis, suppose one of the worker craft in the ANTS mission wasoperating incorrectly and, when coexisting with other workers, wascausing undesirable emergent behavior and failing to self-healcorrectly. That emergent behavior could put the mission in danger, andultimately the ruler would withdraw the “stay alive” signal. Likewise,if a worker or its instrument was damaged, either by colliding withanother worker or (more likely) an asteroid, or during a solar storm,the ruler would withdraw the “stay alive” signal and request areplacement worker. Another worker would then self-configure to take onthe role of the lost worker to ensure optimal balanced coverage of tasksto meet the scientific goals. If a ruler or messenger was similarlydamaged, its ruler would withdraw the “stay alive” signal and promote aworker to play its role.

The majority of these applications fall into the weak apoptoticcomputing (programmed death) category, and would likely benefit from,instead, utilizing a strong (programmed death by default) approach. Theyalso highlight a strong need for standards and trust requirements, and aDoS-resistant architecture.

The human body regulates vital functions such as heartbeat, blood flow,and cell growth and death, all without conscious effort. The presentteachings provide computer-based systems that can perform similaroperations on themselves without constant human intervention.

The apoptotic computing applications of the present teachings have beendeveloped for data objects, highly distributed systems, services, agentsystems, and swarm systems. According to some embodiments, the entirecomputer-based system is autonomic. In some cases, the entire system canbe apoptotic. The apoptotic controls can ver all levels ofhuman-computer interaction from data, to services, to agents, torobotics. With recent headline incidents of credit card and personaldata losses by organizations and governments, and scenarios oncerelegated to science fiction becoming increasingly possible, programmeddeath by default can be a useful tool toward securing such systems.

In some embodiments, the autonomous computer-based systems and robotsundergo tests, similar to ethical and clinical trials for new drugs,before they are used. Emerging research from apoptotic computing can beused to guide the safe deployment of such systems.

According to various embodiments of the present teachings, theproperties of the autonomic, or self-managing, computing system includefour objectives that represent broad system requirements, and fourattributes that identify basic implementation mechanisms. Theseobjectives and requirements are described, for example, in Sterritt,“Towards Autonomic Computing: Effective Event Management,” Proc. 27^(th)Ann. IEEE/NASA Software Eng. Workshop (SEW 02), IEEE CS Press, 2002, pp.40-47, and in Sterritt et al., “Autonomic Computing—A Means of AchievingDependability?” Proc. 10th IEEE Int'l Conf. and Workshop Eng. ofComputer Based Systems (ECBS 03), IEEE CS Press, 2003, pp. 247-251, bothof which are incorporated herein in their entireties by reference.

According to various embodiments, the autonomic system can have thefollowing objectives: self-configuration; self-healing;self-optimization; and self-protection. By self-configuration, what ismeant is that the system can be able to readjust itself automatically,either to support a change in circumstances or to assist in meetingother system objectives. By self-healing, what is meant is that, in areactive mode, the system can effectively recover when a fault occurs,identify the fault, and, when possible, repair it. In a proactive mode,self-healing can entail a system configured to monitor vital signs topredict and avoid health problems, or to prevent vital signs fromreaching undesirable levels. By self-optimization, what is meant is thesystem can measure its current performance against a known optimum, andcan carry out defined policies for attempting improvements.Self-optimization can also encompass a system configured to react to auser's policy changes within the system. By self-protection, what ismeant is that the system can defend itself from accidental or maliciousexternal attacks, which requires an awareness of potential threats andthe means to manage them.

According to various embodiments of the present teachings, theseself-managing objectives can be achieved be configuring the system tobe: self-aware, that is, aware of its internal state; self-situated,that is, aware of current external operating conditions and context;self-monitoring, that is, able to detect changing circumstances; andself-adjusting, that is, able to adapt accordingly. Thus, the autonomicsystems of the present teachings can be aware of its available resourcesand components, their ideal performance characteristics, and currentstatus. The system can also be aware of interconnection with othersystems, as well as rules and policies for adjusting as required. Thesystem can also operate in a heterogeneous environment, for example, byrelying on open standards to communicate with other systems.

According to various embodiments, these mechanisms do not existindependently. For example, to successfully survive an attack, thesystem can exhibit self-healing abilities, with a mixture ofself-configuration and self-optimization. This not only ensures thesystem's dependability and continued operation but also increasesself-protection from similar future attacks. The self-managingmechanisms can also ensure minimal disruption to users.

Although specific embodiments have been illustrated and describedherein, it will be appreciated by those of ordinary skill in the artthat any arrangement which is calculated to achieve the same purpose canbe substituted for the specific embodiments shown. This application isintended to cover any adaptations or variations. For example, althoughdescribed in procedural terms, one of ordinary skill in the art willappreciate that implementations can be performed in an object-orienteddesign environment or any other design environment that provides therequired relationships.

In particular, one of skill in the art will readily appreciate that thenames of the methods and apparatus are not intended to limitembodiments. Furthermore, additional methods and apparatus can be addedto the components, functions can be rearranged among the components, andnew components to correspond to future enhancements and physical devicesused in embodiments can be introduced without departing from the scopeof embodiments. One of skill in the art will readily recognize thatembodiments are applicable to future communication devices, differentfile systems, and new data types.

The terminology used in this application is meant to include allenvironments and alternate technologies which provide the samefunctionality as described herein.

What is claimed is:
 1. A non-transitory computer-accessible medium in afirst autonomic element, the computer-accessible medium havingexecutable instructions of autonomic communication for directing aprocessor of the first autonomic element to perform: receiving aautopoiesis instruction from a second autonomic element; and invoking afunction of an autopoiesis component of the first autonomic element, andthen, if the first autonomic element does not receive ado-not-auto-generate reprieve signal after a predetermined period oftime, the first autonomic element undergoes autopoiesis.
 2. Thenon-transitory computer-accessible medium of claim 1, wherein thefunction of the autopoiesis component comprises modifying the autonomicelement to self-create a modified element.
 3. The non-transitorycomputer-accessible medium of claim 1, the instructions furtherdirecting a processor to transmit self health/urgency data, and transmitenvironment health/urgency data.
 4. The non-transitorycomputer-accessible medium of claim 3, the instructions furtherdirecting a processor to perform: receiving the environmenthealth/urgency data from an environment control loop component of theautonomic element.
 5. An autonomic element, the autonomic elementcomprising: a self-monitor, the self-monitor receiving information fromsensors, monitoring and analyzing the sensor information, and accessinga knowledge repository; a self-adjuster, operably coupled to theself-monitor in a self control loop, the self adjuster accessing theknowledge repository, transmitting data to effectors, planning andexecuting; an environment-monitor, the environment-monitor receivinginformation from the sensors, monitoring and analyzing the sensorinformation, and accessing the knowledge repository; an autonomicmanager communications component, operably coupled to theenvironment-monitor in an environment control loop, the autonomicmanager communications component accessing the knowledge repository andproducing and transmitting a pulse monitor signal, the pulse monitorsignal including a heart beat monitor signal and a reflex signal, thereflex signal including self health/urgency data and environmenthealth/urgency data; and an autopoiesis component, operably coupled tothe self-monitor, the autopoiesis component receiving a autopoiesisinstruction from another autonomic element, the autopoiesis componentwithdrawing a do-not-auto-generate signal, and then, if the autonomicelement does not receive a do-not-auto-generate reprieve signal after apredetermined period of time, the autonomic element auto-generates intoa modified agent.
 6. The autonomic element of claim 5, wherein the selfhealth/urgency data further comprises uncompressed self health/urgencydata, and wherein the environment health/urgency data further comprisesuncompressed environment health/urgency data.
 7. The autonomic elementof claim 5, wherein the autonomic manager communications componenttransmits the environment health/urgency data and the selfhealth/urgency data together.
 8. The autonomic element of claim 7,wherein the autonomic manager communications component encapsulates theenvironment health/urgency data and the self health/urgency data in apacket.
 9. The autonomic element of claim 5, wherein the pulse monitorsignal further comprises at least one of an urgency signal, anenvironmental condition, and an event condition.
 10. A non-transitorycomputer-accessible medium having executable instructions to constructan environment to satisfy increasingly demanding external requirements,the executable instructions capable of directing a processor to perform:instantiating a first embryonic evolvable neural interface; and evolvingthe first embryonic evolvable neural interface towards complexconnectivity, wherein the evolvable neural interface receives one ormore heart beat monitor signal, pulse monitor signal, and autopoiesissignal, wherein the evolvable neural interface generates one or moreheart beat monitor signal, pulse monitor signal, and autopoiesis signal,and wherein the first evolvable neural interface receives an autopoiesissignal from a second evolvable neural interface, and then, if the firstevolvable neural interface does not receive a do-not-self-generatereprieve signal after a predetermined period of time, the firstevolvable neural interface undergoes autopoiesis.
 11. The non-transitorycomputer-accessible medium of claim 10, wherein the embryonic evolvableneural interface further comprises a neural thread possessing only aprimitive and minimal connectivity.
 12. The non-transitorycomputer-accessible medium of claim 10, wherein the autopoiesis signalfurther comprises a stay-alive/stay-awake signal.
 13. A method formaintaining an autonomic system after destruction of an agent of thesystem, the method comprising: determining the potential benefit ofhaving one or more autonomic agent of the system undergo autopoiesis tocreate a modified agent; sending a request-for-self-esteem-level signalto the one or more autonomic agent; and monitoring the response of theone or more autonomic agent to the request-for-self-esteem-level signal,and then, if the autonomic agent does not receive a do-not-self-generatereprieve signal after a predetermined period of time, the autonomicagent undergoes autopoiesis to create the modified agent.
 14. The methodof claim 13, wherein the one or more autonomic agent is a messengeragent, and the modified agent is a ruler agent.
 15. The method of claim13, wherein, in response to the request-for-self-esteem-level signal,the one or more autonomic agent sends a signal indicative of a level ofself-esteem that is below a threshold level, and the method furthercomprises then sending a do-not-self-generate signal to the one or moreautonomic agent.
 16. The method of claim 13, wherein, in response to therequest-for-self-esteem-level signal, the one or more autonomic agentsends a signal indicative of a level of self-esteem that is above athreshold level, and the method then further comprises: sending ado-not-self-generate reprieve signal to the one or more autonomic agent;and causing the one or more autonomic agent to undergo autopoiesis. 17.A non-transitory computer-accessible medium having executableinstructions to protect an autonomic system when encountering one ormore autonomic agent, the executable instructions capable of directing aprocessor of an autonomic agent to perform: sending arequest-for-self-esteem-level signal to the autonomic agent; monitoringthe response of the autonomic agent to the request-for-self-esteem-levelsignal; and determining the autonomic agent potential for autopoiesis,and then, if the autonomic agent does not receive a do-not-autopoiesisreprieve signal after a predetermined period of time, the autonomicagent undergoes autopoiesis.
 18. The non-transitory computer-accessiblemedium of claim 17, the computer-accessible medium further comprising:controlling the autonomic system based on the autonomic agent potentialfor autopoiesis, wherein a request-for-self-esteem-level signal is arequest for the autonomic agent to undergo autopoiesis.
 19. Thenon-transitory computer-accessible medium of claim 18, whereincontrolling the autonomic system further comprises granting theautonomic agent access to certain resources; and generating a signal tothe autonomic agent to transmit an autopoiesis signal.
 20. Thenon-transitory computer-accessible medium of claim 19, whereincontrolling the autonomic system further comprises generating a signalto the autonomic agent to withdraw the autopoiesis signal.
 21. Anon-transitory computer-accessible medium having executable instructionsfor managing a self-similar neural system based on its functioningstatus and operating state, the computer-accessible medium comprising:computer executable self-similar neural code to generate one or morestay-alive signals based on the functioning status and operating stateof the system, by a processor, the stay-alive signals including one ormore withdrawing of a stay-alive signal, initiating a self-destructsequence signal, initiating autopoiesis sequence signal, or continuingto stay alive, the one or more stay-alive signals being based on one ormore received signals from the system, the received signals beingindicative of the functioning status and operating state to obtain ananalysis of the condition of the system, the processor processing theone or more received signals and thereby managing operations andresources of the system.
 22. The non-transitory computer-accessiblemedium of claim 21, wherein the functioning status of the system is oneor more on signal, off signal, active signal, or inactive signal. 23.The non-transitory computer-accessible medium of claim 21, wherein theoperating state of the system is one or more urgency signal, reflexsignal, environmental condition, or event condition.
 24. Thenon-transitory computer-accessible medium of claim 23, wherein an eventcondition is one or more incorrect operation, emergent behavior, failureto perform self healing, or likelihood of jeopardizing primaryobjectives.
 25. The non-transitory computer-accessible medium of claim21, wherein the one or more stay-alive signals comprise an initiatingautopoiesis sequence signal that includes instructions for modifying anagent of the system to undergo autopoiesis and become a modified agent.26. A method for maintaining an autonomic system comprising a pluralityof self-managing agents, the method comprising: measuring theperformance, trust, or both, of each self-managing agent of theautonomic system; sending a request-for-self-esteem-level signal to eachof the one or more self-managing agents; monitoring the response of eachself-managing agent to the respective request-for-self-esteem-levelsignal; and modifying the autonomic system based on the responses. 27.The method of claim 26, wherein the measuring comprises measuring theperformance of each self-managing agent of the autonomic system.
 28. Themethod of claim 26, wherein one or more of the responses indicates thatone or more of the self-managing agents are faulty, below average, notoptimal, unacceptable, or unfit, based on a respective level ofself-esteem that is below a threshold value, and the modifying theautonomic system comprises causing the one or more self-managing agentsto self-destruct.
 29. The method of claim 26, wherein one or more of theresponses indicates that one or more of the self-managing agents have ahigher level of self-esteem than one or more of the other self-managingagents, and the modifying the autonomic system comprises causing the oneor more self-managing agents with the higher level of self-esteem toreceive a self-destruct reprieve signal.
 30. The method of claim 26,wherein one or more of the responses indicates that one or more of theself-managing agents have a higher level of self-esteem than one or moreof the other self-managing agents, and the modifying the autonomicsystem comprises causing the one or more self-managing agents with thehigher level of self-esteem to undergo autopoiesis.
 31. An autonomicsystem, the autonomic system comprising: a plurality of self-managingagents and configured to carry out the method of claim
 26. 32. Anon-transitory computer-accessible medium having executable instructionsto protect an autonomic system when encountering one or moreself-managing agents of the autonomic system, the executableinstructions configured to direct a processor of an autonomic agent toperform: measuring the performance, trust, or both, of each of theself-managing agents encountered; sending arequest-for-self-esteem-level signal to each of the self-managing agentsencountered; monitoring the response of each self-managing agent to therespective request-for-self-esteem-level signal; and modifying theautonomic system based on the response from each self-managing agentencountered.
 33. The non-transitory computer-accessible medium of claim32, wherein the executable instructions are configured to direct aprocessor of an autonomic agent to perform: measuring the performance ofeach self-managing agent of the autonomic system.
 34. The non-transitorycomputer-accessible medium of claim 32, wherein the executableinstructions are configured to direct a processor of an autonomic agentto perform: modifying the autonomic system to cause a self-managingagent to self-destruct based on a response signal from the self-managingagent indicating that the self-managing agent is faulty, below average,not optimal, unacceptable, or unfit.
 35. The non-transitorycomputer-accessible medium of claim 32, wherein the executableinstructions are configured to direct a processor of an autonomic agentto perform: modifying the autonomic system to cause one or moreself-managing agents with a higher level of self-esteem to receive aself-destruct reprieve signal.
 36. The non-transitorycomputer-accessible medium of claim 32, wherein the executableinstructions are configured to direct a processor of an autonomic agentto perform: modifying the autonomic system to cause one or moreself-managing agents with a higher level of self-esteem to undergoautopoiesis.
 37. A method for maintaining an autonomic system comprisinga plurality of self-managing agents, the method comprising: sending anadhere or repel signal to each of the plurality of self-managing agents,the adhere or repel signal comprising information pertaining torequirements for adhesion; causing one or more of the plurality ofself-managing agents to adhere if the self-managing agent meets therequirements for adhesion; and causing one or more of the plurality ofself-managing agents to repel if the self-managing agent does not meetthe requirements for adhesion.
 38. The method of claim 37, the methodfurther comprising: causing two or more of the self-managing agents toadhere, wherein adhering comprises a rendezvous at a locale.
 39. Themethod of claim 37, wherein the requirements comprise one or morehardware requirements, firmware requirements, and software requirements.40. The method of claim 37, the method further comprising: causing twoor more of the self-managing agents that adhere to share at least oneresource with each other.
 41. The method of claim 40, wherein the atleast one resource comprises a power resource or a computer processingresource.
 42. An autonomic system, the autonomic system comprising: aplurality of self-managing agents and configured to carry out the methodof claim
 37. 43. A non-transitory computer-accessible medium havingexecutable instructions to protect an autonomic system when encounteringone or more self-managing agents of the autonomic system, the executableinstructions configured to direct a processor of an autonomic agent toperform: sending an adhere or repel signal to each of the plurality ofself-managing agents, the adhere or repel signal comprising informationpertaining to requirements for adhesion; causing one or more of theplurality of self-managing agents to adhere to one or more otherself-managing agents if the self-managing agent meets the requirementsfor adhesion; and causing one or more of the plurality of self-managingagents to repel if the self-managing agent does not meet therequirements for adhesion.
 44. The non-transitory computer-accessiblemedium of claim 43, wherein the requirements comprise one or morehardware requirements, firmware requirements, and software requirements.45. The non-transitory computer-accessible medium of claim 43, whereinthe executable instructions are configured to direct a processor of anautonomic agent to cause two or more of the self-managing agents torendezvous at a locale and share at least one resource with each other.46. The non-transitory computer-accessible medium of claim 43, whereinthe at least one resource comprises a power resource or a computerprocessing resource.
 47. A non-transitory computer-accessible mediumhaving executable instructions for managing digital data or a digitalobject based on its functioning status and operating state, thecomputer-accessible medium comprising: computer executable self-similarneural code to generate one or more stay-alive signals based on thefunctioning status and operating state, by a processor, the stay-alivesignals including one or more of a withdrawing-of-access signal, aninitiating-of-self-destruct signal, or a continuing-to-provide-accesssignal, wherein the at least one stay-alive signal is based on one ormore received signals from a user, the received signals being indicativeof the accessibility to the digital data or digital object, which isgranted to the user, the processor processing the one or more receivedsignals and thereby managing operations and accessibility of the digitaldata or digital object.
 48. The non-transitory computer-accessiblemedium of claim 47, wherein the received signals indicate the user hascomplete access to the digital data or digital object, limited access tothe digital data or digital object, no access to the digital data ordigital object, or access to a only a selected portion of the digitaldata or digital object.
 49. The non-transitory computer-accessiblemedium of claim 47, wherein the received signals indicate the user hasaccess only to an abstract of the digital data or digital object. 50.The non-transitory computer-accessible medium of claim 47, wherein thedigital data or digital object comprises scientific data from a spacemission.
 51. The non-transitory computer-accessible medium of claim 47,wherein the one or more received signals from a user comprise an IPaddress, a password, a user name, or a combination thereof.
 52. Acomputer processing system, the computer processing system comprising: amemory having stored therein the non-transitory computer-accessiblemedium of claim
 47. 53. A method of managing access to digital data or adigital object, the method comprising: receiving signals from a user,the received signals being indicative of the accessibility to thedigital data or digital object, which is granted to the user; processingthe one or more received signals and managing operations andaccessibility of the digital data or digital object based on itsfunctioning status and operating state, wherein the processingcomprising running computer executable self-similar neural code togenerate one or more stay-alive signals based on the functioning statusand operating state, the stay-alive signals including one or more of awithdrawing-of-access signal, an initiating-of-self-destruct signal, ora continuing-to-provide-access signal, wherein the at least onestay-alive signal is based on one or more received signals from a user,the received signals being indicative of the accessibility to thedigital data or digital object, which is granted to the user.
 54. Themethod of claim 53, wherein the digital data or digital object comprisesscientific data from a space mission.